Airfoil-Performance-Degradation Prediction Based on Nondimensional Icing Parameters
Bibliographic record
Abstract
A physics-based empirical correlation between icing conditions and the corresponding drag coefficient was developed for NACA 0012 airfoils, and compared to other three existing prediction methods. The correlation was developed based on experimental aerodynamic databases of iced airfoils, and derived using statistical methods. The correlation model also provides drag coefficients for varying angles of attack for a given icing condition. The calculated drag coefficients resulted in 33.40% mean absolute deviation with respect to reference data from three different experimental databases. To validate the proposed degradation model and to further extend the database for helicopter-rotor performance degradation, rotating ice-accretion experiments were conducted. Four ice shapes obtained at the NASA Icing Research Tunnel were reproduced on a 53.34-cm-chord, 1.37-m-radius NACA 0012 rotor blade at the Adverse Environment Rotor Test Stand facility. Ice-shape molding and casting techniques were introduced to capture delicate ice features, such as ice feathers. The iced-airfoil castings were tested in a dry-air wind tunnel. The drag-coefficient comparison between the proposed analytical determination method and the experimental results from both rotor ice testing and icing-wind-tunnel testing showed to be satisfactory, ranging from 5 to 25% depending on the icing condition. The effect of ice feathers on drag degradation was investigated. Ice-feather formation can account for up to 25% of the drag introduced by ice accretion before stall.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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 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".