Introducing hinge mechanisms to one compressive-mode piezoelectric energy harvester
Bibliographic record
Abstract
In this paper, a hinge mechanism is introduced into one compressive-mode piezoelectric energy harvester to improve its performance. First, the concept of implementing hinge mechanisms is introduced on a high-efficiency compressive-mode piezoelectric energy harvester (HC-PEH). Second, a numerical model based on the piezoelectric constitutive equation and the Duffing oscillator equations is formulated to obtain voltage responses, velocity responses, and the fundamental frequency and bandwidth. Then, a prototype is fabricated to validate the results of the model. Depending on the number of hinges applied to the HC-PEH, three cases are investigated: fully hinged, partially hinged, and clamped. In both numerical modeling and experimental studies, the HC-PEH prototypes in the three cases are exposed to frequency-sweep excitations to illustrate the dynamic and transduction behaviors. The results demonstrate that the overall performance in the hinged cases is improved significantly compared to that in the clamped case. The output voltage and output power are increased by 2–3 times and up to 5 times, respectively, and fundamental resonant frequency is lowered to below 20 Hz. Furthermore, it is shown that the operational bandwidth is widened by up to 37%.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".