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Record W2056380783 · doi:10.1109/tmech.2014.2362685

Broadening the Frequency Bandwidth of a Tire-Embedded Piezoelectric-Based Energy Harvesting System Using Coupled Linear Resonating Structure

2014· article· en· W2056380783 on OpenAlex
Soheil Sadeqi, Siamak Arzanpour, Kambiz Haji Hajikolaei

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE/ASME Transactions on Mechatronics · 2014
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsEnergy harvestingBandwidth (computing)VibrationAcousticsSpring (device)StiffnessPiezoelectricityResonance (particle physics)Energy (signal processing)Materials sciencePhysicsEngineeringStructural engineeringTelecommunications

Abstract

fetched live from OpenAlex

The efficiency of single-degree-of-freedom (SDOF) vibration-based energy harvesters significantly drops when the resonance frequency of the harvester is different from that of the ambient vibration. In this study, a novel piezoelectric-based energy harvesting mechanism is introduced for rotary motion applications, which can generate power over a broad range of angular velocities of the wheel. The proposed design, which comprises a coupled spring-mass system attached to a PZT beam, has the advantage that it can easily be tuned in an off-line position by simply changing the tip mass and/or spring stiffness. A theoretical and experimental study is undertaken to check the performance of the proposed design for the range of speeds typical of commercial tires. It is shown that by tuning the resonance frequency of the mass-spring system the design can significantly increase the frequency bandwidth of the energy harvester.

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.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.735
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.214
Teacher spread0.202 · 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