Influence of Hot Top Height on Macrosegregation and Material Yield in a Large-Size Cast Steel Ingot Using Modeling and Experimental Validation
Bibliographic record
Abstract
The effect of the hot top height on the formation of positive and negative macrosegregation patterns, the ingot quality, and the material yield during solidification of a 12 MT cast ingot made of a Cr-Mo-low alloy steel was investigated. A 3D numerical simulation of the process was conducted using finite element modeling. A full-size 12 MT ingot was cut off from its center in the longitudinal direction, and a large cross-section was sliced into small samples. The chemical mapping of all the elements in the steel composition was obtained for all samples and compared with the model predictions for validation purposes. The influence of the increase in hot top height on the liquid metal velocity field, size and shape of vortexes, cooling rate of the liquid, and liquidus temperature was determined. Results revealed that increasing the hot top height by 165 mm increased the solidification time, fluid velocity in regions including the hot top and ingot bottom, and decreased the local liquidus temperature. The combination of all the above resulted in an overall decrease in positive and negative macrosegregation of more than 6% and an increase in ingot quality. The results are interpreted based on the interactions between the transport of solute and heat coupled with the flow driven by thermo-solutal convection and shrinkage-induced flow.
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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".