Broadband energy harvesting through a piezoelectric beam subjected to dynamic compressive loading
Bibliographic record
Abstract
This paper investigates the design and analysis of a broadband piezoelectric energy harvester that uses a simply supported piezoelectric beam compressed by dynamic loading. The beam is restrained at one end and carries a moving mass at the other end where a magnetic force is applied axially. Taking advantage of the flexibility of the slender beam and the nonlinearity of the magnetic force, the design aims to enhance the harvester's functionality with a broad frequency bandwidth. Both theoretical and experimental investigations are performed in this study over a range of excitation frequencies. Specifically, the electromechanical model of the harvester is analytically developed by means of the energy-based method and the extended Hamilton's principle. Using the derived model, a parametric study is carried out to obtain the harvester's voltage response under parametric excitations. Furthermore, the effects of various parameters on the harvester's voltage response are examined. A prototype harvester is fabricated and experimentally tested. The theoretical model is validated against experimental data to confirm the harvester's nonlinear response behaviors and enhanced capabilities. Both simulation and experiment illustrate that the harvester exhibits a softening nonlinearity and hence a broad frequency bandwidth with large-amplitude voltage response. It is also shown from numerical simulations that the harvester's performance can be further improved by properly selecting the end mass and reducing the mechanical damping. The present findings demonstrate that dynamic compressive loadings can be successfully utilized to increase the harvester's voltage output and frequency bandwidth.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".