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Fully-Coupled Modelling and Experimental Validation of Quarter Wavelength Resonator with Piezoelectric Backplate in Vibro-Acoustic Energy Harvesting

2025· article· en· W4412066392 on OpenAlexaboutno aff
Muhammad Hatifi Mansor, M. S. M. Sani, Mohd Firdaus Hassan, Lihua Tang

Bibliographic record

VenueInternational Journal of Automotive and Mechanical Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsnot available
FundersUniversiti Malaysia Pahang
KeywordsQuarter (Canadian coin)PiezoelectricityResonatorAcousticsMaterials scienceWavelengthEnergy (signal processing)Energy harvestingOptoelectronicsPhysicsGeography

Abstract

fetched live from OpenAlex

Converting and harvesting the unwanted sounds produced by noise, especially in busy cities, can solve the issue of sound pollution and provide renewable power sources for low-power electronics. Although sound energy is freely available, it is hard to harvest due to its relatively low energy density compared to other sources. To enhance the efficiency of acoustic energy harvesting, particularly in the low-frequency range. The intgration of an optimised resonator is essential. This research study explores the performance of a vibroacoustic energy harvester incorporating a straight tube quarter-wavelength resonator coupled with a piezoelectric patch mounted on a flexible backplate. A fully coupled finite element model (FEM) was developed to capture the interaction between acoustic field, structural dynamic and piezoelectric transduction, and its predictions were validated against experimental results. The numerical model yielded a maximum output voltage of 1.41 V/Pa at 112 Hz, closely matching experiment findings of 1.44 V/Pa at 106 Hz under an incident sound pressure level of 90 dB. The proposed modelling framework demonstrates strong predictive capability and provides a robust basis for the design and optimisation of low-frequency acoustic energy harvester based on quarter-wavelength resonator configuration.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.204
Teacher spread0.199 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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