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Record W4321018688 · doi:10.1088/1361-665x/acbcb1

Design and analysis of a d15 mode piezoelectric energy generator using friction-induced vibration

2023· article· en· W4321018688 on OpenAlexafffund
Yu Xiao, Sviatoslaw Karnaoukh, Nan Wu

Bibliographic record

VenueSmart Materials and Structures · 2023
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPiezoelectricityVibrationMaterials scienceAcousticsGenerator (circuit theory)VoltageEnergy (signal processing)Piezoelectric sensorPower (physics)Energy harvestingMechanicsPhysicsElectrical engineeringThermodynamicsEngineering

Abstract

fetched live from OpenAlex

Abstract Research works have been conducted on transverse and longitudinal mode piezoelectric energy generation to collect energy from ambient vibrations. However, the inconsistency with the frequency of the energy source and low output power density remain problems for high energy output. In this work, we propose a shear mode piezoelectric energy generator, which utilizes the friction-induced vibration (FIV) and high shear mode piezoelectric coefficient to improve the energy output. A piezoelectric coupled FIV mathematical model is developed to accurately calculate the dynamic vibration response and voltage output. The dynamic voltage response is validated by experiment, and it proves the possibility of continuous friction-induced high-frequency vibration. The energy generation process is evaluated by transient charging simulation of a storage capacitor through an iteration process, which was experimentally validated in the literature. Parameter studies have been conducted to investigate the influences of the piezoelectric patch dimensional parameters, vibration system parameters, friction model parameters, methods of electrical connections, and different piezoelectric materials on the energy generation performance to provide guidance for better design. Under ideal experiment conditions with proper parameters, a volume of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>6.25</mml:mn> <mml:mo>×</mml:mo> <mml:mrow> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>8</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> m 3 PZT4 piezoelectric material indicates root mean square (RMS) charging power density of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>5.38</mml:mn> <mml:mo>×</mml:mo> <mml:mrow> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>3</mml:mn> </mml:msup> </mml:mrow> </mml:math> Wm −3 and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>4.70</mml:mn> <mml:mo>×</mml:mo> <mml:mrow> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>3</mml:mn> </mml:msup> </mml:mrow> </mml:math> Wm −3 with electrically in parallel and electrically in series, respectively. While using the same amount of material and structural setup, the single crystal PMN-PT piezoelectric material shows RMS charging power density of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>2.72</mml:mn> <mml:mo>×</mml:mo> <mml:mrow> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>4</mml:mn> </mml:msup> </mml:mrow> </mml:math> Wm −3 and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>2.58</mml:mn> <mml:mo>×</mml:mo> <mml:mrow> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>4</mml:mn> </mml:msup> </mml:mrow> </mml:math> Wm −3 with electrically in parallel and electrically in series, correspondingly. These promising results demonstrate that close to W-level RMS charging power output may be realized by structure optimization of energy generator design and incorporating multiple generators together for operation. Possible incorporation into vehicle braking systems can be considered to utilize the wasted friction energy, and it may offer an energy supply for low-power wireless devices.

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 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 score0.203
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.024
GPT teacher head0.238
Teacher spread0.214 · 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.

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".

Quick stats

Citations18
Published2023
Admission routes2
Has abstractyes

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