A Comparison of Low‐ and High‐Fidelity Models for Tail Rotor Icing Phenomena
Bibliographic record
Abstract
A low-fidelity model for modeling ice accretion phenomena over two-bladed teetering rotors has been developed. In this approach, the blades are assumed to be rigid, and the flapping motion is caused by the inertial, centrifugal, and aerodynamic forces acting on the blade section. The aerodynamic forces are computed using a table look-up of precomputed airfoil lift and drag coefficients as a function of effective angle of attack. Following the analysis of the rotor without icing effects, ice shapes at selected radial locations on the rotor are computed. The impact of ice shapes on the two-dimensional (2D) lift and drag characteristics is estimated using a 2D computational fluid dynamics analysis. Finally, the rotor is reanalyzed using the low-fidelity model with the iced airfoil lift and drag characteristics. Comparisons with test data and a higher fidelity model are also presented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".