In-Flight Icing Risk Management Through Computational Fluid Dynamics-Icing Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In thiswork, an in-depth analysis of the occurrence of in-flight icing accidents and incidents is conducted to identify high-risk flight conditions. These conditions are investigatedmore thoroughly using a computational fluid dynamics model of a representative airfoil. This model permits the recreation of experiments conducted in the controlled environment of the icing wind tunnel. The ice accumulations and resulting aerodynamic performance degradations of the airfoil are computed for a range of pitch angles and flight speeds. These simulations reveal substantial performance losses such as reduced maximum lift, and decreased stall angle. From these results, an icing hazard analysis tool is developed, using risk management principles, to evaluate the dangers of in-flight icing for a specific aircraft based on the atmospheric conditions it is expected to encounter, as well as the effectiveness of aircraft certification procedures.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it