A Review of Fluid Flows in Liquid Metal Processing and Casting Operations
Bibliographic record
Abstract
Fluid flows have proved to be an integral part of many metallurgical processing operations.Metal, slag, and gas flows invariably affect the viability, effectiveness, and efficiency, of our reactor vessels.The performance characteristics of our blast furnaces and steelmaking vessels, such as BOF's, OBM's, ladles, tundishes, and the moulds of continuous casting machines, are all strongly influenced by such flows.Similarly, liquid metal quality and cast micro-structures, are also bound up with the way fluids have flowed and interacted.In all these aspects, the rapid evolution in our techniques and abilities to mathematically and physically model single and multi-phase flows and their attendant heat and mass transfer processes, have contributed significantly to our understanding, and ability, to control and improve these metallurgical processing operations, and to develop new and better processes.The evolution and application of computational fluid dynamics (CFD) over the past four decades has been particularly impressive.The author's many fine Japanese graduate students have made very valuable contributions to this new field of research for Process Metallurgists, as well as to the founding and scientific support of the McGill Metals Processing Centre, MMPC, following their return to Japan.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".