Modelliing of transport phenomena for improved steel quality in a delta shaped four strand tundish
Bibliographic record
Abstract
Physical and mathematical modelling studies were performed, in order to analyze various transport phenomena occurring during steel making tundish operations. Their effects on liquid metal quality were reported. A full-scale water model of a twelve tonne, delta shaped, four strand, billet caster tundish was used for physical modelling. The commercial code ANSYS FLUENT 12 was used for carrying out mathematical modelling. The tundish used in the present study is a full scale replica of that operated at the RTIT/QIT plant in Sorel Tracy, Canada and is located at MMPC's water modelling laboratory at McGill University. It is a long lasting fact that the flow pattern within a tundish greatly affects the output metal quality. As such the insertion of flow modifiers in a tundish is a common practice. In the present study, eighteen different arrangements of flow modifier systems (combinations of impact pad and dams) were considered, and mathematical modelling was performed to predict the inclusion removal efficiency for each tundish configuration. A new dimensionless number (Gu) has been proposed, which is a good measure of steel cleanliness. During melt transfer from the ladle to the tundish, inert gas is injected into the ladle shroud, just below the slide gate, so as to prevent aspiration of ambient air. The effect of inert gas shrouding on the fluid flow patterns and slag movements have been numerically predicted by using a 3D mathematical model, and then validated with water model experiments. The effect of the alignment of the ladle shroud during melt transfer was also studied, using a 3D mathematical model, supported by subsequent water model experiments. It was demonstrated that a slight bias from the vertical can be very detrimental to steel quality. Remedial measures have been suggested. During typical steelmaking tundish operations, conditions are generally non-isothermal. Variable heat losses take place from the free surface and from the walls of the tundish. Similarly, during a ladle change, the steel poured in from the new ladle will tend to be at a higher temperature than the liquid steel remaining in the tundish. Flow patterns change under non-isothermal conditions and hence affect output steel quality. A thorough study has been performed to visualize the effect of thermal gradients on fluid flow patterns, and temperature distributions generated within the delta shaped tundish.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".