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Record W2795196907 · doi:10.5539/jmsr.v7n2p26

Modeling, Simulation and Optimization of Piezoelectric Bimorph Transducer for Broadband Vibration Energy Harvesting in Multi-Beam and Trapezoidal Approach

2018· article· en· W2795196907 on OpenAlexvenueno aff
Nan Chen, Vishwas Bedekar

Bibliographic record

VenueJournal of Materials Science Research · 2018
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsnot available
FundersMiddle Tennessee State University
KeywordsMultiphysicsMaterials scienceAcousticsBroadbandBandwidth (computing)Energy harvestingTransducerBimorphCantileverPower densityPiezoelectricityBeam (structure)VibrationPower (physics)Finite element methodOpticsStructural engineeringComputer sciencePhysicsEngineeringTelecommunicationsComposite material

Abstract

fetched live from OpenAlex

The objective of the research is to design a broadband energy harvester device through the multi-beam approach and non-linear trapezoidal geometry approach. The performance of the composite piezoelectric PZT-PZN polycrystalline ceramic material is simulated using COMSOL Multiphysics, and results are compared using series configuration of a composite bimorph energy harvester which vibrates at its 1st fundamental frequency. We chose a five cantilever multibeam harvester to demonstrate that individual fundamental modes of the beams can achieve a broader frequency band and generate power. Authors also show that composite trapezoidal beam design leads to high power density broadband frequency response. The multibeam approach resulted in broader bandwidth of 18 Hz while generating a power density of 0.0913 mW/cm3 whereas the trapezoidal shape generated 2.3 – 2.5 mW/cm3 with a bandwidth of 4 to 6 Hz. Authors believe that these results could help design broadband energy harvesters to enhance power density as well as bandwidth.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.102
GPT teacher head0.351
Teacher spread0.249 · 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 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

Citations8
Published2018
Admission routes1
Has abstractyes

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