Estimation of Thermophysical Properties as Functions of Temperature in Rapid Radial Solidification of Metallic Alloys
Bibliographic record
Abstract
Recent global efforts to produce lightweight electrified vehicles have motivated the push toward advanced lightweight materials which led to the creation of novel alloys optimized for use in high-pressure die casting (HPDC). HPDC enables the fabrication of near-net-shape automotive parts, significantly reducing or eliminating additional machining steps. A key feature of HPDC is the extremely fast cooling that forces the alloy to solidify within only a few seconds. Because of these rapid cooling conditions, it becomes essential to accurately evaluate the thermophysical behavior of newly designed lightweight alloys during severe quenching. Precisely quantifying these material properties is crucial for properly controlling HPDC operations and for building reliable numerical models that simulate filling and solidification. The thermophysical characteristics of such alloys vary markedly with temperature, especially when the material undergoes the fast solidification typical of HPDC. Therefore, understanding how these properties change with temperature during intense cooling becomes a critical requirement in alloy development. To address this need, a dedicated experimental system was designed to solidify molten metal samples under controlled and variable cooling conditions by applying multiple impinging water jets. An inverse heat-transfer algorithm was formulated to extract temperature-dependent thermal conductivity and diffusivity of the alloy as it solidifies under rapid cooling. To verify the reliability of both the inverse model and the measurements, experiments were performed using pure Tin, a reference material with well-documented thermophysical properties. The computed thermophysical properties of Tin were benchmarked against values reported in the literature and demonstrated reasonable consistency, with a maximum deviation of 13.6%.
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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 | 0.001 |
| 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".