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Charge Redistribution in Flextensional Piezoelectric Energy Harvesters

2014· article· en· W1987960490 on OpenAlexaff
Zhengbao Yang, Jean W. Zu

Bibliographic record

VenueApplied Mechanics and Materials · 2014
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEnergy harvestingPiezoelectricityAcousticsVibrationTransducerActuatorMechanical energyElectric potential energyFinite element methodRedistribution (election)Materials scienceEnergy (signal processing)Power (physics)EngineeringElectrical engineeringStructural engineeringPhysics

Abstract

fetched live from OpenAlex

Harvesting ambient wasted energy has been in recent years a prominent research endeavor, aimed at providing alternative energy sources for low-power electronic mobile devices. Among various solutions, piezoelectric energy harvesters have attracted major attention due to the scalability, high-efficiency and the universal presence of vibration sources. In this paper, we studied the charge redistribution phenomenon in piezoelectric energy harvesters employing flextensional structures numerically and experimentally. A finite element model was developed firstly to study the mechanical and electrical response of flextensional transducers. The simulation results were then validated by a corresponding experiment. The research reveals that energy is dissipated in the process that charge flows from the high potential region to the low potential region. The electrode shape has a significant effect on the efficiency, and therefore should be considered fully when designing new energy harvesters. This study also assists in the design of flextensional sensors and actuators.

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: none
Teacher disagreement score0.617
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.181
Teacher spread0.174 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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