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Record W2263536351 · doi:10.1115/detc2015-47334

A Novel Multi-Directional Nonlinear Piezoelectric Energy Harvester Coupled With Nonlinear Conditioning Circuits

2015· article· en· W2263536351 on OpenAlexaff
Zhengbao Yang, Jean W. Zu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEnergy harvestingPiezoelectricityRodVibrationMaterials scienceNonlinear systemAcousticsCantileverMaximum power principleMechanical energyPower (physics)Electrical engineeringEngineeringVoltagePhysicsComposite material

Abstract

fetched live from OpenAlex

Energy harvesting from vibrations has become, in recent years, a recurring target of a quantity of research to achieve self-powered operation of low-power electronic devices. However, most of energy harvesters developed to date, regardless of different transduction mechanisms and various structures, are designed to capture vibration energy from single predetermined direction. To overcome the problem of the unidirectional sensitivity, we proposed a novel multi-directional nonlinear energy harvester using piezoelectric materials. The harvester consists of a flexural center (one PZT plate sandwiched by two bow-shaped aluminum plates) and a pair of elastic rods. Base vibration is amplified and transferred to the flexural center by the elastic rods and then converted to electrical energy via the piezoelectric effect. A prototype was fabricated and experimentally compared with traditional cantilevered piezoelectric energy harvester. Following that, a nonlinear conditioning circuit (self-powered SSHI) was analyzed and adopted to improve the performance. Experimental results shows that the proposed energy harvester has the capability of generating power constantly when the excitation direction is changed in 360. It also exhibits a wide frequency bandwidth and a high power output which is further improved by the nonlinear circuit.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.650
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.033
GPT teacher head0.231
Teacher spread0.198 · 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.

Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations1
Published2015
Admission routes1
Has abstractyes

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