Numerical simulation of the galvanizing process during GA to GI transition
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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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".