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Record W2743280168 · doi:10.5539/jmsr.v6n4p5

Modeling, Simulation and Optimization of Piezoelectric Bimorph Transducer for Broadband Vibration Energy Harvesting

2017· article· en· W2743280168 on OpenAlexvenueno aff
Nan Chen, Vishwas Bedekar

Bibliographic record

VenueJournal of Materials Science Research · 2017
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsnot available
FundersMiddle Tennessee State University
KeywordsMultiphysicsMaterials sciencePiezoelectricityBimorphEnergy harvestingAcousticsVibrationTransducerVoltageEnergy (signal processing)Finite element methodComposite materialStructural engineeringElectrical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

We demonstrate the detailed analysis for conversion of piezoelectric properties into compliance matrix and simulate a series bimorph configuration for vibration based energy generation. Commercially available software COMSOL Multiphysics was used to apply boundary conditions for optimization of geometric parameters such as length, width and thickness of piezoelectric layer to study voltage and power characteristics of the harvester. The resulting energy harvester was found to generate 1.73 mW at 53.4 Hz across a 3MW load with an energy density of 13.08mJ/cm3. We also investigated feasibility of this model by comparing it with existing experimental data of known piezoelectric ceramic compositions and found good correlation between the two.

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 score0.010

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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.099
GPT teacher head0.367
Teacher spread0.268 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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