Identification of Aeroelastic Parameters for Cormorant Tail Rotor Structures based on Vibration Measurement in the Stationary Frame
Bibliographic record
Abstract
The teetering tail rotor of the Canadian CH-149 Cormorant helicopter fleet experienced limit cycle oscillation (LCO) phenomenon in certain adverse flight conditions in which the transient high amplitude vibration resulted in significant increase of dynamic strain to the tail rotor structures. This could contribute to pre-mature damage of the composite half hub among other causes. Therefore it is required to evaluate the integrity of the tail rotor structure using realistic aeroelastic parameters of the tail rotor in the rotating frame. This paper presents a novel center frequency scaling factor theory and related parameter identification methodologies in order to estimate and track the variation of the critical aeroelastic parameters in the rotating frame during LCO events based on vibration information measured exclusively in the stationary frame. This information would enable update of the tail rotor stability diagram in order to confidently evaluate the impact of LCO events to the structural integrity of the Cormorant tail rotor structures. Based on the developed procedures and identified parameters, recommendations are provided for data analysis and techniques to improve the fidelity of results from aeroelastic simulation analysis for the Cormorant tail rotor structures without the need to install sensors in the rotating frame. © 2012 AIAA.
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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) | 0.000 | 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".