Nonlinear vibration analysis of the high-efficiency compressive-mode piezoelectric energy harvester
Bibliographic record
Abstract
Power source is critical to achieve independent and autonomous operations of electronic mobile devices. The vibration-based energy harvesting is extensively studied recently, and recognized as a promising technology to realize inexhaustible power supply for small-scale electronics. Among various approaches, the piezoelectric energy harvesting has gained the most attention due to its high conversion efficiency and simple configurations. However, most of piezoelectric energy harvesters (PEHs) to date are based on bending-beam structures and can only generate limited power with a narrow working bandwidth. The insufficient electric output has greatly impeded their practical applications. In this paper, we present an innovative lead zirconate titanate (PZT) energy harvester, named high-efficiency compressive-mode piezoelectric energy harvester (HC-PEH), to enhance the performance of energy harvesters. A theoretical model was developed analytically, and solved numerically to study the nonlinear characteristics of the HC-PEH. The results estimated by the developed model agree well with the experimental data from the fabricated prototype. The HC-PEH shows strong nonlinear responses, favorable working bandwidth and superior power output. Under a weak excitation of 0.3 g (g = 9.8 m/s2), a maximum power output 30 mW is generated at 22 Hz, which is about ten times better than current energy harvesters. The HC-PEH demonstrates the capability of generating enough power for most of 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".