Ab‐initio Predictions of Interfacial Heat Flows during the High Speed Casting of Liquid Metals in Near Net Shape Casting Operations
Bibliographic record
Abstract
Abstract When metals are cast into solid shapes, the quality of the solid casting depends on many things, but heat flow management is a critical factor. It is relatively easy to predict heat flows through the liquid metal, and the solid mould, but heat flows through the interconnecting interface have been much more difficult to quantify. In the present work, following a review of our progress up to date on near net shape casting, the approach is to model this interfacial resistance from first principles. By conducting experiments in which liquid aluminum is cast at high speed (∼0.5 m/s), onto a copper substrate, fitted with extremely sensitive embedded thermocouples, heat fluxes from the first moments of metal contact, to final freezing of the strip, have been measured. Similarly, by using a 3D profilometer that is able to rapidly characterize and quantify the surface topography of a substrate, to ±1 µm, one can have the necessary data to mathematically model the transfer of heat from the overlaying metal, through the interfacial layer, into the copper substrate. The thermal model briefly described, makes the assumption of point contact between pyramidal peaks of the metal substrate and molten metal, with gas pockets trapped in the “valleys” of the substrate, through which heat must be transferred by conduction. Ab‐initio instantaneous heat fluxes predicted in this way proved to be in good agreement with those measured, provided adjustments were made for expansion of the “air gap”.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".