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Record W2901322934 · doi:10.1115/smasis2018-8197

Experimental Studies on a Compressive-Mode Piezoelectric Transducer for Low-Frequency and Wide-Band Energy Harvesting

2018· article· en· W2901322934 on OpenAlexaff
Zhongjie Li, Hani E. Naguib

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTransducerBandwidth (computing)PiezoelectricityAcousticsEnergy harvestingAmplitudeVibrationTrussBifurcationMaterials scienceNonlinear systemAdded massPhysicsEnergy (signal processing)EngineeringOpticsStructural engineeringTelecommunications

Abstract

fetched live from OpenAlex

In this paper, we report a low-frequency and wide-bandwidth piezoelectric energy transducer. The transducer is designed based on a piezoelectric plate, a truss mechanism, a spring-mass system and a stopper. The spring-mass system receives kinetic energy from excitation and induces forces, which are further transmitted, amplified by the truss mechanism and applied onto the piezoelectric plate. The stopper is added to truncate the amplitude of the mass. The mass and the stopper interact through impacts. The impact force triggers dynamic bifurcation in the transducer. By taking advantage of the superharmonic resonances and nonlinearity born from the bifurcation, the transducer is able to work efficiently with a wide bandwidth. Through experiment studies on a fabricated prototype, the lowest resonant frequency is around 3.2Hz with the peak-peak voltage output up to 55V. The bandwidth of the transducer is approximately 4.5Hz out of our targeted frequency domain [2.5Hz, 10Hz], broadened by up to 20 times compared to that of the linear system without the stopper.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.029
GPT teacher head0.290
Teacher spread0.261 · 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 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".

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Citations0
Published2018
Admission routes1
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