Characterization of heat transfer and its effect on solidification in water cooled LPDC of wheels
Bibliographic record
Abstract
Abstract Computational process modelling has become an important engineering tool in the casting industry to predict the solidification sequence in complex castings. Used properly, this tool can help reduce manufacturing costs. One of the challenging issues in developing casting simulations of the low pressure die casting (LPDC) process for automotive wheels is to quantify the heat transfer coefficients (HTC) within the cooling channels in a die. When water is used as the cooling media, the HTCs exhibit a complex, non-linear behaviour due to the boiling phenomena that occur making it possible to extract a significant amount of heat from the die in a short period of time and influence the solidification of a wheel. Primarily, constant heat transfer coefficients have been used to describe this heat transfer in casting models up until now, but an opportunity exists to improve the transient description of heat transfer in channels cooled with water. In this paper, HTC’s in a lab-scale physical analogue model of die cooling will be characterized as a function of initial die temperatures.
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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) | 0.000 | 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) | 0.002 | 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".