FE Modelling and Prediction of Macrosegregation Patterns in Large Size Steel Ingots: Influence of Filling Rate
Bibliographic record
Abstract
In the present work, the influence of filling rate on macrosegregation in a 40-Metric Ton (MT) ingot of a high-strength low-carbon steel was studied using finite element (FE) simulation. The modelling of the filling and solidification processes were realized with a two-phase (liquid-solid) multiscale 3D model. The liquid flow induced by the pouring jet, the thermosolutal convection, and the thermomechanical deformation of the solid phase were taken into consideration. Two filling rates were examined, representing the upper and lower manufacturing limits for casting of large size ingots made of high strength steels for applications in energy and transportation industries. The evolution of solute transport, as well as its associated phenomena throughout the filling and cooling stages, were also investigated. It was found that increasing the filling rate reduced macrosegregation intensity in the upper section, along the centerline and in the mid-radius regions of the ingot. The results were analyzed in the framework of heat and mass transfer theories, liquid flow dynamics, and macrosegregation formation mechanisms.
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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 | 0.001 | 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 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".