Applications of Computational Fluid Dynamics (CFD) in iron- and steelmaking: Part 1
Bibliographic record
Abstract
All operations in process metallurgy involve complex phenomena comprising momentum, heat, and/or mass transport; iron- and steelmaking is not an exception. Transport phenomena, i.e. fluid flows, heat transfer and mass transfer, play a dominant role in process metallurgy since their respective laws govern the kinetics of the various physical phenomena occurring in ironmaking and in steelmaking. These phenomena include such events as three-phase reactions, entrainment of slag and gas in liquid steel, vacuum degassing, alloy melting and mixing, the movements and flotation of inclusions, melt temperature losses, residence times in a metallurgical reactor, erosion of refractory linings, etc. In all these aspects, the evolution in our techniques and abilities to model single and multiphase flows and their attendant heat and mass transfer processes has contributed significantly to our understanding and effectively operating these processes, to designing improvements, and to developing new processes. To be ignorant of these matters can doom a processing operation to the scrap heap of metallurgical failures. Computational fluid dynamics (CFD) and computational heat and mass transfer has been a very effective tool over the last three decades, for modelling iron- and steelmaking processes, starting from the blast furnace up to continuous casting and beyond. With the advent of commercial CFD packages and ever increasing computational power through parallel processing, CFD has now become the dominant approach for predicting various aspects in iron- and steelmaking processes. In Part 1 of this review paper, the applications of CFD in ironmaking processes are thoroughly reviewed, discussed and critiqued. In Part 2, fluid flows and CFD in steelmaking and steel casting processes are similarly reviewed and critiqued.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".