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Record W2161705781 · doi:10.2514/6.2012-1665

Modeling and Analysis of Piezoelectric Energy Harvesting from Helicopter Blades

2012· article· en· W2161705781 on OpenAlexaff
Wander Gustavo Rocha Vieira, Fred Nitzsche, Carlos De Marqui

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsCarleton University
FundersFundação de Amparo à Pesquisa do Estado de Minas GeraisConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsEnergy harvestingPiezoelectricityEnergy (signal processing)Computer scienceEnergy analysisEngineeringAerospace engineeringAcousticsElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

Converting aeroelastic vibrations into electricity for low-power generation has received growing attention over the past few years. Helicopter blades with embedded piezoelectric elements can provide electrical energy to power small electronic components. In this paper, the non-linear modeling and analysis of a composite cantilevered helicopter blade with embedded piezoceramics is presented for energy harvesting. A resistive load is considered in the electrical domain of the problem (generator circuit) in order to quantify the electrical power output. The shunt damping effect due to the resistive dissipation is also investigated. The non-linear electromechanical model of a rotating beam with embedded piezoceramics is derived based on the Variational-Asymptotic Method (VAM). The coupled non-linear rotary system is solved in the time-domain. A generalized-� integration method is used to guarantee numerical stability, adding numerical damping at high frequencies. The results are verified against numerical and experimental results reported in the literature. The electromechanical behavior of the coupled rotating blade is investigated for increasing rotating speeds (stiffening effect) and also for different electrical boundary conditions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.213
Teacher spread0.196 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations3
Published2012
Admission routes1
Has abstractyes

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