Improvement of carbon segregation in billet by final-EMS, based on segmented continuous casting model
Bibliographic record
Abstract
Based on magnetohydrodynamics and solidification theory, a three-dimensional segmented continuous casting model, coupling electromagnetic phenomena, fluid flow, heat transfer and solidification, and solute distribution, was established using COMSOL. Simulation results revealed a positive segregation, with a degree of approximately 1.05, was observed beneath the meniscus, and as the position moved downward, a mild negative segregation, with a degree of approximately 0.97, emerged at a distance of about 10 mm from the wall due to the washing effect on the solidification front. Within the natural convection zone, an asymmetric distribution of carbon segregation between the loose and fixed sides was evident, influenced by the thermal solutal buoyancy. With an increase in the electrical current applied for the final electromagnetic stirring (F-EMS), the Lorentz forces responsible for agitating the molten steel became more pronounced, resulting in a more uniform distribution of carbon. This indicated that the introduction of F-EMS accelerated convection between the molten steel and the mushy zone, facilitating the redistribution of solutes. At an F-EMS current of 250 A–8 Hz, the carbon distribution exhibited the highest uniformity, reducing the degree of segregation at the billet centre from 1.30 to 1.22, a decrease of 0.08. The incorporation of F-EMS effectively addressed centreline segregation issues, and the calculated carbon concentration distribution under both F-EMS and non-F-EMS conditions closely matched experimental measurements.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".