Applications of Computational Fluid Dynamics (CFD) in iron- and steelmaking: Part 1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it