MEMS piezoelectric energy harvester design and optimization based on Genetic Algorithm
Bibliographic record
Abstract
MEMS piezoelectric energy harvesters, due to their unique features in power density and ease of fabrication, are known as one of the most promising solutions for providing unlimited power sources for low-power electronic applications. In this paper, the analytic equations to estimate generated voltage amount by a piezoelectric cantilever under various vibrations are presented. The comparison between the analytic equations and finite element method (FEM) simulations confirms over 85% accuracy in the estimation of generated voltage amount for the presented analytic model, which can be used as a fitness function of Genetic Algorithm (GA). We have used the GA, which is a design automation technique for optimization problems, to improve the energy harvesting efficiency by optimizing the dimension of the piezoelectric energy harvesters. The observed results from the optimized physical aspects of MEMS piezoelectric energy harvester illustrate an enhancement of energy harvesting efficiency by a factor of 2.13. The proposed method can be considered as a general and efficient technique for enlarging conversion efficiency of piezoelectric energy harvesting devices.
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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 | 0.001 | 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".