Evaluation of Visual Failure versus Aerodynamic Limit for a Snow Contaminated Anti-Iced Wing Section during Simulated Takeoff
Bibliographic record
Abstract
Under contract to Airlines for America (A4A), APS Aviation Inc. (APS), in collaboration with the National Research Council of Canada (NRC), completed an aircraft ground icing exploratory research project at the NRC 3 m × 6 m Wind Tunnel in Ottawa in January 2019. The purpose of this project was to investigate the feasibility of using aerodynamic data to evaluate the performance of contaminated anti-icing fluid, rather than the traditional visual fluid failure indicators that are used to develop Holdover Times (HOTs). The aerodynamic performance of a supercritical airfoil model with anti-icing fluids and snow contamination was evaluated against the clean, dry performance of the airfoil in order to calculate the associated aerodynamic penalty. The visual failure of the fluid was also evaluated for each run, and the visual and aerodynamic results were compared against each other for each contamination exposure time. The results demonstrated that the visual failure of a fluid was not directly linked to the aerodynamic performance of the fluid, and the margin between the two was a function of the fluid used, the model configuration, and the test temperature. The results support the premise that in specific operational conditions, certain fluids may offer protection for a period of time after the visual failure. This margin of protection is likely dependent on the same variables as the current holdover times, including precipitation type and intensity, fluid type, and outdoor air temperature and aircraft configuration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".