Frost formation over a cold plate with icephobic coatings
Bibliographic record
Abstract
Abstract: In this study, a new experimental set-up has been developed to investigate by flow visualizations the frost formation and growth on a cold plate. Seven environmental conditions, varying the air temperature, relative humidity, and velocity and the plate temperature, are considered. For the uncoated plate, the experimental data on the temporal evolution of the frost layer thickness enable to validate a pseudo 1D model formerly developed. Three bioinspired icephobic surfaces including superhydrophobic, SLIPS and self-lubricant coatings are then compared to the base case without coating for five environmental conditions. Superhydrophobic coating provides the highest retardation time for the formation and growth of frost compared to the SLIPS and self-lubricant coatings whatever the environmental conditions. It appears as a valuable way to delay the formation of frost and improve the performance of refrigeration systems based on eutectic plates, used for the transport of food products.
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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 | 0.000 |
| 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) | 0.000 | 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".