Numerical investigation of dross formation and minimization in continuous galvanizing baths
Bibliographic record
Abstract
This contribution presents numerical investigations into some aspects affecting the formation of dross during continuous galvanizing. It is well known that dross formation depends on the bath composition, temperature and flow dynamics. However, experimental observation indicates that top dross formation is affected by oxidation at the surface of the bath as the use of an inert gas in air knifes greatly reduces dross generation. In this work numerical simulations are used to investigate the flow dynamics of the gas in the vicinity of the bath surface in order to obtain correlations between the gas velocity and the propensity for oxidation and dross formation. Simulations are first shown for a bench scale experiment simulating top dross formation for which experimental data are available. The experiment consists of a crucible containing liquid zinc with various aluminum and iron contents. The flow in the crucible is generated by the rotation of a propeller immersed into the liquid zinc and then air or inert gas is projected to the surface of the liquid metal to determine the effect on oxidation and dross formation. Dross formation is then correlated to the size of the exposed surface and to the relative velocity between the liquid zinc and the gas. Numerical results are also shown for the gas flow dynamics inside air knifes and in the vicinity of the bath surface. This provides a map of the relative gas/liquid velocity at bath surface which then can be used to estimate the propensity for oxidation and top dross formation.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".