A new model of spongy icing from first principles
Bibliographic record
Abstract
A new icing model has been developed to predict the sponginess (liquid fraction) and growth rate of freshwater ice accretions growing under a surface film of unfrozen water. This model is developed from first principles and does not require experimental sponginess data to tune the model parameters. The model identifies icing conditions that include no accretion, dry accretion, glaze accretion, spongy nonshedding, and spongy shedding regimes. It is a steady state model for a stationary vertical cylinder intercepting horizontally directed spray. The model predicts both the accretion mass growth flux and the accretion sponginess. The model results suggest that spongy shedding and spongy nonshedding regimes are common under the high liquid flux conditions typical of freshwater ship icing. Moreover, the unfrozen liquid incorporated into the spongy ice matrix can substantially increase the ice accretion load over that which would be predicted purely thermodynamically. Despite differences in the experimental setup, the model's performance compares well with two independent freshwater experimental data sets for icing on horizontal rotating cylinders. The model performs well in its prediction of both accretion sponginess and growth rate. The model predicts sponginess with a variation in liquid mass fraction of about 0.2–0.5, over the range of air temperature of 0°C to −30°C, in agreement with observations.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".