Mechanisms for Downstream Ice Growth
Bibliographic record
Abstract
Even though aircraft icing has been an active area of research for many years due to its public safety ramifications, there are still gaps in the knowledge base, such as in the ice accretion process. Ice prediction codes have been developed and generally can capture the gross features of the ice shape for many areas of parameter space. However, there are still features of the ice shapes that are not captured and not well understood. For example, current icing codes have difficulty in predicting the ice or frost that develops beyond the impingement limits from a standard trajectory analysis. This is a strong indicator that there are other physical mechanisms that lead to ice growth in these areas, which require further investigation. The present study focuses on the effects of relative humidity and shed vortices from the ice surface on the downstream ice growth. Relative humidity was found to be a secondary effect in the direct impingement regions due to the time scales involved. However, exposure to supersaturated air for long periods can lead to localized ice/frost growth aft of the primary ice shape. It was also found that the vortices shed from ice surface or vortices entrapped within the ice roughness can alter the trajectory paths of the droplets and potentially change the ice growth process. The altered paths result in impingement on areas that are beyond the direct impingement region.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".