Ab-initio Predictions of Interfacial Heat Fluxes in Horizontal Single Belt Casting (HSBC), Incorporating Surface Texture and Air Gap Evolution
Bibliographic record
Abstract
The purpose of this study was to develop ab-initio mathematical and computational models, aimed at predicting instantaneous heat fluxes when a liquid metal or alloy first comes into contact with a colder substrate during near net shape casting processes. Fully computational models were developed to determine whether the measured instantaneous heat fluxes associated with the strip casting of aluminum alloys on copper substrates could be inferred from first principles. For this, strip cast aluminum surfaces were physically analyzed using a 3-D Profilometer, so as to provide the detailed surface textural information needed for the mathematical modeling. It was shown that the modeled mould surface characteristics, such as pyramid height and number of contact points per mm2, are critical in determining the peak heat fluxes achieved during metal/mould contact. Reducing pyramid heights and/or increasing the number of contact points are beneficial in enhancing interfacial heat fluxes.The mechanism of air pocket formation was also explored through mathematical modeling. The volume expansion of entrapped air was deduced to be the main reason for “air pockets” forming on the strip's bottom surface. A new method for predicting air gap evolution was proposed in which a fixed grid system and an anisotropic thermal conductivity model were used. The computational models allow for various scenarios to be effectively studied, and for experimental curves to be matched against “predicted” curves. Finally, copper moulds with macroscopically textured surfaces were tested, and it was found that these surfaces were effective in expelling entrapped air to adjacent grooves, and in enhancing overall interfacial heat fluxes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".