Performance of tapered cantilever piezoelectric energy harvester based on Euler–Bernoulli and Timoshenko Beam theories
Bibliographic record
Abstract
The cantilever beam configuration plays an important role in the power extracted from the vibration-based piezoelectric energy harvesters. Although it has already been proven that triangular and trapezoidal shapes optimize and improve the electrical output of the piezoelectric energy harvesters, the impact of other shapes has not been considered. It is necessary to figure out which shape can provide the maximum amount of power and efficiency, as well. In this article, a complete study regarding the influence of non-uniform theories using both Timoshenko and Euler–Bernoulli beams for both unimorph and bimorph states is carried out. The width and height of the cantilever beams are changed based on the degree of the polynomial function. To solve the equations, finite element method with the application of two different elements including 4-degree-of-freedom model and 8-degree-of-freedom model is adopted. Based on the analysis, it can be concluded that by increasing the degree of non-uniformity and slenderness ratio, the amount of harvested electrical output rises. Moreover, the difference between two beam theories is significant for thick beams with small slenderness ratios. In addition, the effects of non-uniformity including the tapering ratio described by polynomial functions on the efficiency of piezoelectric energy harvesters are studied.
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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 | 0.001 |
| Open science | 0.001 | 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".