On the Impact of Microsegregation Model on the Thermophysical and Solidification Behaviors of a Large Size Steel Ingot
Bibliographic record
Abstract
The impact of microsegregation models on thermophysical properties and solidification behaviors of a high strength steel was investigated. The examined microsegregation models include the classical equilibrium Lever rule, the extreme non-equilibrium Scheil-Gulliver, as well as other treatments in the intermediate regime proposed by Brody and Flemings, Clyne and Kurz, Kobayashi and Ohnaka. Based on the comparative analyses performed on three representative regions with varied secondary dendrite arm spacing sizes, the classical equilibrium Lever rule and non-equilibrium Scheil scheme were employed to determine the thermophysical features of the studied steel, using the experimentally verified models from literature. The evaluated thermophysical properties include effective thermal conductivity, specific heat capacity and density. The calculated thermophysical data were used for three-dimensional simulation of the casting and solidification process of a 40 metric ton steel ingot, using FEM code Thercast®. The simulations captured the full filling, the thermo-mechanical phenomena and macro-scale solute transport in the cast ingot. The results demonstrated that Lever rule turned out to be the most reasonable depiction of the physical behavior of steel in study in large-size cast ingot and appropriate for the relevant macrosegregation simulation study. The determination of the model was validated using the experimentally measured top cavity dimension, the thermal profiles on the mold outside surface by means of thermocouples, and the carbon distribution patterns via mass spectrometer analysis.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".