Mathematical modelling of temperature evolution in the hot rolling of steel
Bibliographic record
Abstract
The CANMET-McGill Mathematical Model has been expanded and improved to provide a more comprehensive predictive tool for simulation of temperature evolution in the hot rolling of steel. The model uses a Windows(TM)-based, user-friendly, graphical interface and the explicit finite-difference method in two dimensions to simulate the rolling of flat product. Data input options were expanded to allow an initial temperature gradient in the steel to be rolled, the inclusion of a runout table in the calculations, specific chemical compositions and a choice of emissivity values. A novel, chemistry-dependent thermal conductivity term was developed to account for the variation in heat conduction through the thickness of the steel and to ensure that the temperature of the entire cross-section is accurately predicted. In addition, the model now calculates a heat of transformation for phase changes from austenite to ferrite, pearlite and bainite. An automated self-calibration module was included to facilitate the calibration of the model's predictions to empirically obtained temperature setpoints. The model's temperature predictions were validated against both industrial and laboratory data with excellent results.
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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 | 0.001 | 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".