Performance of non-uniform functionally graded piezoelectric energy harvester beams
Bibliographic record
Abstract
The appearance of functionally graded piezoelectric materials has solved the lamination problem of the conventional piezoelectric structures. Functionally graded piezoelectric materials are the new materials with unexplored capabilities. This article theoretically investigates the effects of non-uniformity on the performance of the functionally graded piezoelectric material cantilever beams subjected to harmonic excitation. The governing equations are derived based on Timoshenko and Euler–Bernoulli beam theories. The finite element method with the application of superconvergent element is employed here for the discretization and the vibration analysis of the system. This model is validated by comparing the numerical results with the experimental results of piezoelectric energy harvesters of conventional shapes available in the open literature. Parametric studies are carried out with respect to the effects of tapering ratios, the degree of non-uniformity, load resistance, and the volume fraction parameter on the electrical output power and the fundamental resonance frequency. It was observed that the application of diverging beams noticeably enhances the power output per mass of piezoelectric element extracted while decreases the natural frequency which is advantageous for scavenging energy from ambient surroundings. The results reveal that there is an optimal value for the non-homogeneous parameter leading to the maximized harvested energy under different operating conditions.
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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) | 0.001 | 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".