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Record W4323304613 · doi:10.1177/09574565231156995

Nonlinear dynamic response of bimorph piezoelectric energy harvester with functionally graded porous core

2023· article· en· W4323304613 on OpenAlexaff
Narges Akbar, M. Soltani

Bibliographic record

VenueNoise & Vibration Worldwide · 2023
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsVibrationPiezoelectricityGalerkin methodVoltageMagnetBimorphCantileverAcousticsNonlinear systemCore (optical fiber)Beam (structure)Parametric statisticsHarmonic balanceMaterials sciencePiezoelectric coefficientMechanicsStructural engineeringEngineeringPhysicsFinite element methodElectrical engineeringMathematicsComposite material

Abstract

fetched live from OpenAlex

This study deals with piezoelectric energy harvesters carrying the magnet at the free end while a magnifier connected them to the base. The harvester consists of a cantilever beam with Functionally Graded (FG) porous foam core and two piezoelectric faces while taking into account the von-Karman strains and the magnetic interaction of two magnets. The governing equation has been developed using Hamilton’s principle and reduced-order via the Galerkin method. Frequency response of vibrations and voltage derived using harmonic balance method and the adjusted model has been confirmed by parametric studies of the lumped parameter model. A parametric study is also performed to expose the effects of porosity coefficient and pattern, magnifier ratios, and excitation level on the responses. Results show that by correct selection of the magnifier parameters, the proposed harvester can provide a higher output over a wider frequency band. Also, the core porosity increases the flexibility of the beam and raised the voltage by a factor of 1.8.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.014
GPT teacher head0.221
Teacher spread0.207 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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