Influence of the Hot-Top Thermal Regime on the Severity and Extent of Macrosegregation in Large-Size Steel Ingots
Bibliographic record
Abstract
The influence of hot-top designs with different heat capacities on the distribution of positive and negative macrosegregation was investigated on a 12 metric tonne (MT) cast ingot made using Cr-Mo low-alloy steel. The three-dimensional finite element modeling code THERCAST® was used to simulate the thermo-mechanical phenomena associated with the solidification process, running from filling the mold until complete solidification. The model was validated on an industrial-scale ingot and then utilized to evaluate the influence of the thermal history of the hot-top, a crucial component in the cast ingot setup. This assessment aimed to comprehend changes in solidification time, temperature, and heat flux—all of which contribute to the determination of macrosegregation severity. The results showed that preheating the hot-top had a minor effect on solidification time, while modifications of thermal conductivity in the hot-top region increased the solidification time by 31%, thereby significantly affecting the macrosegregation patterns. The results are discussed and interpreted in terms of the fundamental mechanisms governing the kinetics of solidification and macrosegregation phenomena.
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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) | 0.000 | 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".