Predicting Thermal Contact Resistance Between Molten Metal Droplets and a Solid Surface
Bibliographic record
Abstract
Thermal contact resistance between molten metal droplets (aluminum alloy 380 and bismuth) and solid plates (steel and brass) was measured experimentally. The diameter of the droplets was 4mm, and droplet impact velocity ranged between 1 and 3m∕s. Substrate temperature was varied from 25to300°C and roughness from 0.06to5.0μm. Substrate temperature variation under impacting droplets was measured using fast temperature sensors that had a response time of 40ns and recorded substrate temperatures at five different radial locations under each droplet. Thermal contact resistance during the first few milliseconds of impact was obtained by matching measured surface temperature variation with an analytical solution of the one-dimensional transient heat conduction equation. An analytical model of the deformation of a free liquid surface in contact with a rough solid was used to calculate the true area of contact between them and, thereby, the thermal contact resistance. Test results agreed well with predictions from the analytical model. Thermal contact resistance values ranged from 10−7to3×10−6m2K∕W, increasing with surface roughness and decreasing with rising impact velocity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".