Study on Blade Property Measurement and Its Influence on Air/Structural Loads
Bibliographic record
Abstract
In this study, the structural properties of Second Higher-Harmonic Aeroacoustic Rotor Test blades are determined using state-of-the-art test techniques. The measurement includes bending and torsion stiffnesses, section geometric offsets, and mass and inertia properties. Several Second Higher-Harmonic Aeroacoustic Rotor Test blades, including the original instrumented blade used for the wind-tunnel test campaign in 2001, as stated by Yu et al. (The HART-II Test: Rotor Wakes and Aeroacoustics with Higher-Harmonic Pitch Control (HHC) Inputs—The Joint German/French/Dutch/US Project, Proceedings of the 58th American Helicopter Society (AHS) Annual Forum, American Helicopter Soc., Montreal, June 2002) are used for the activity. A finite element-based cross-section analysis combined with an x-ray computer tomography technique is employed for the cases where no mechanical measurement is available or attempted. The resulting structural properties are correlated against the earlier estimated values, which have extensively been used in the literature for the validation of the Second Higher-Harmonic Aeroacoustic Rotor Test rotor. A substantial deviation is observed between the present measurement and the earlier property result. The comprehensive rotor dynamics analysis is performed to quantify the impact of the measured blade properties on the aeromechanics behavior of the rotor. The location of the center of gravity is demonstrated to be the most influential factor affecting the deviation and the sensitivity of the aeroelastic response of the rotor.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".