Estimation of molecular and thermodiffusion coefficients for non‐ideal molten metal alloys and its implication in solidification process
Bibliographic record
Abstract
In this paper an expression is proposed for the estimation of thermodiffusion factor in liquid metal alloys. The expression can be readily used given that it requires properties that could be easily obtained from the physical properties of the mixture constituents, such as viscosity and molar volume. The predictive power of the proposed expression, as well as other pertinent models is examined against the experimental data. The estimated thermodiffusion factor is then used to study thermo‐solutal convection in an enclosure filled with molten Sn–Bi alloy by solving the transport equations numerically. Two simulations were carried out in a vertical rectangular cell encapsulated by a quartz container: top heating and bottom heating. The sidewalls in both cases were exposed to the external natural convection and surface radiation to the ambient. Numerical results show that in the top heating case, the distribution of temperature and concentration are linear, but species segregation occurs due to the thermodiffusion effect. In the bottom heating case, boundary‐driven convective flow develops with a large Rayleigh number (Ra) where an increase in the Ra number negates the thermodiffusion effect due to the development of strong mixing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".