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Record W2726893684 · doi:10.4050/jahs.60.042001

Piezoelectric Deicing System for Rotorcraft

2015· article· en· W2726893684 on OpenAlexfundno aff
Éric Villeneuve, Derek Harvey, D. G. Zimcik, Roger J. Aubert, Jean Perron

Bibliographic record

VenueJournal of the American Helicopter Society · 2015
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsActuatorHelicopter rotorRotor (electric)PiezoelectricityVibrationIcingStructural engineeringPower (physics)AcousticsPiezoelectric sensorEngineeringAerospace engineeringMechanical engineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

Deicing using piezoelectric actuators is considered as a potential solution to the development of low-energy ice protection systems for rotorcraft. This type of system activates resonant frequencies of a structure using piezoelectric actuators to generate sufficient stress to break the bond between the ice and the substrate. First, a numerical method was validated to assist the design of such systems. Numerical simulations were performed for the case of a flat plate and validated experimentally. The model was then used to study important design parameters such as actuator positioning and activation strategies, and it was concluded that positioning actuators at antinode locations, and activating them in phase with those antinodes to obtain maximum displacements for a given vibration mode. The findings were then used to apply piezoelectric deicing to structures more representative of a helicopter rotor blade. The method was implemented on a thinned Bell 206 main rotor blade and a Bell 206 tail rotor blade. Deicing performance was demonstrated in an icing wind tunnel. Power input to the actuators was below 19 kW/m2 (12 W/inch2) for all structures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.017
GPT teacher head0.232
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations42
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Helicopter SocietySame topicIcing and De-icing TechnologiesFrench-language works237,207