‘Smart spring’ identification for hovering rotor aeroelastic-stability augmentation
Bibliographic record
Abstract
Abstract This work deals with tailoring of adaptive material included at the roots of hingeless helicopter rotor blades to be used in individual blade control (IBC) strategies. Usually, IBC strategies involving the use of adaptive materials either consider adaptive material embedded in the blade structure for inducing strain deformations, or apply adaptive actuators for controlling segments of the blade (e.g. for moving trailing-edge flaps). Here, the adaptive material is used to provide augmentation of modal damping in a passive control approach, that can be conveniently tuned so as to make it the most suitable for the actual rotor configuration under examination. The presentation of a procedure for tailoring this ‘smart spring’ is the aim of the paper. The aeroelastic blade model considered consists of a cantilever slender beam undergoing flap, lead-lag and torsional motion, coupled with a strip theory approach for the prediction of the aerodynamic loads, based on the very low frequency approximation of the pulsating-free-stream Greenberg’s theory. Starting from this model and applying the Galërkin method, generalised mass, damping and stiffness matrices of the basic blade, as well as the incremental generalised mass, damping and stiffness matrices due to the ‘smart spring’ have been determined, the latter depending on the ‘smart spring’ inertial and elastic characteristics. It will be shown that the application of an optimal control criterion, followed by a low frequency-approximation observer, yields the identification of the most suitable ‘smart spring’ characteristics for augmentation of rotor blade aeroelastic stability. The validity of this procedure will be demonstrated by numerical results concerning the stability analysis of two hovering blade configurations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".