Numerical simulation of the galvanizing process during GA to GI transition
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Bibliographic record
Abstract
Abstract This paper presents the application of a three‐dimensional finite element solution algorithm for the prediction of the velocity, temperature and species concentration fields in an industrial continuous galvanizing bath. Simulations were carried out using a parallel CFD software developed at IMI‐NRC. The turbulent flow, heat and mass transfer has been solved using a high Reynolds number k –ε model. Simulations were carried out for the case when the density of the molten metal depends only on the temperature and also for the case when both temperature and Al concentration affect the density. When considering the buoyancy effect of the Al concentration, differences are especially apparent during the melting of ingots with high Al content. Otherwise, thermal effects are dominant. The continuous monitoring of the temperature and the Al and Fe content in an industrial bath was used to validate the flow, temperature and compositional variations. A period of three hours, corresponding to three different ingot additions, was simulated successfully, resulting in a good agreement of the temperature and compositional variations. Copyright © 2006 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.
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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 | 0.000 | 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 it