Experimental Study of a Piezoelectric De-Icing System Implemented to Rotorcraft Blades
Bibliographic record
Abstract
A four-year project investigating the use of piezoelectric actuators as a vibration-based low power de-icing system has been initiated at the Anti-Icing Materials Laboratory. The work done preceding this investigation consisted of studying, numerically and experimentally, the system integration to a flat plate structure, the optimal excitation of the system, the resonant structural modes and the shear stress amplitudes to achieve de-icing for that structure. In this new investigation, the concepts and conclusions obtained on the flat plate structure were used to design and integrate the system into a rotating blade structure. An experimental setup was built for de-icing tests in rotation within an icing wind tunnel, and a finite-element numerical model adapted to the new geometry of the blade was developed based on the expertise accumulated using previous flat plate structure analysis. Complete de-icing of the structure was obtained in the wind tunnel using the developed de-icing system, and its power consumption was estimated. The power consumption was observed to be lower than the currently used electrothermal systems. The finite-elements numerical model was therefore used to study the case of a full-scale tail rotor blade and showed that the power reduction of the system could be significantly higher for a longer blade, confirming, therefore, the relevance of further de-icing investigations on a full-scale tail 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| 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".