Modelling performance of four‐strand, 12 t, delta shaped continuous casting tundish fitted with different flow modifying arrangements for better steel quality
Bibliographic record
Abstract
The performance of a 12 t, delta shaped, four‐strand, billet casting tundish was investigated using a full scale (1∶1) isothermal model using water as the simulating fluid for steel. Three different modelling experiments were carried out. Quantifiable parameters like ‘number of slag beads’ in transient physical modelling of slag entrainment, or ‘residual ratio of inclusions’ (RRI %), in steady state mathematical modelling of inclusion removal and ‘mean residence time’, ‘dead volume fraction’, etc., in steady state mathematical computation of residence time distributions, were used as the performance indicators. Results for three different flow modifying dam arrangements were considered and compared with those of a bare tundish. Computational fluid dynamic analysis showed that different flow modifying dam arrangements significantly alters the flow pattern within the tundish. On the basis of these performance indicators, the best arrangement was identified. The assumption in this paper that similar conclusions can be drawn from experiments carried out either in transient or in steady state conditions was verified. It was shown that both transient physical modelling experiments and the steady state mathematical predictions, point to the same conclusion.
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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 | 0.000 |
| 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 | 0.001 | 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".