Modeling and parametric study of a force-amplified compressive-mode piezoelectric energy harvester
Bibliographic record
Abstract
Piezoelectric energy harvesters have great potential for achieving inexhaustible power supply for small-scale electronic devices. However, the insufficient power-generation capability and the narrow working bandwidth of traditional energy harvesters have significantly hindered their adoption. To address these issues, we propose a nonlinear compressive-mode piezoelectric energy harvester. We embedded a multi-stage force amplification mechanism into the energy harvester, which greatly improved its power-generation capability. In this article, we describe how we first established an analytical model to study the force amplification effect. A lumped-parameter model was then built to simulate the strong nonlinear responses of the proposed energy harvester. A prototype was fabricated which demonstrated a superior power output of 30 mW under an excitation of 0.3 g ([Formula: see text] m/s 2 ). We discuss at the end the effect of geometric parameters that are influential to the performance. The proposed energy harvester is suitable to be used in low-frequency weak-excitation environments for powering wireless sensors.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".