Optimizing Energy Output of Piezoelectric Microcantilevers
Bibliographic record
Abstract
This work presents some new methods in optimizing electrical energy, harvested using a micro piezoelectric cantilever. Both mechanical and electrical aspects have been considered. Mechanically, two items have been considered to maximize the generated voltage: geometry of the cantilever and placement of the electrodes. It has been shown that for given sizes of length and width of the harvester and for a given natural frequency, the output voltage can be increased by adjusting the thickness of the beam and the proof mass and consequently increasing the amplitude of vibration. As well, the placement of the electrodes plays a very important role in optimizing output voltage. It has also been shown that piezoelectric cantilevers with shorter top electrodes induce higher voltage than cantilevers with longer top electrodes. Overall results agree with the analytical equations reported in literature so far. Moreover, distribution of top electrodes along the width of the cantilever has been taken into consideration. It has been shown how output voltage can be approximately doubled by using two narrower top electrodes along the width of the cantilever. All analysis in this work was carried out in ANSYS. In this research, to improve the electrical efficiency, diodes have been considered in the circuit to reduce electrical losses in comparison to rectifiers which have been used in conventional harvesters. Applying these methods to particular test cases, a 71% increase in output voltage was observed for the case of geometry optimization, a 116% increase was observed for the case of shortening the top electrode and losses in the electrical circuit were reduced by approximately 50% by using diodes comparing to using rectifiers. While these results focused on cantilever based harvesters, the ideas contained are equally applicable to other structures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".