Fully-Coupled Modelling and Experimental Validation of Quarter Wavelength Resonator with Piezoelectric Backplate in Vibro-Acoustic Energy Harvesting
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".