Optimized energy harvesting through piezoelectric functionally graded cantilever beams
Bibliographic record
Abstract
Abstract The emergence of piezoelectric functionally graded materials (PFGMs) has sparked the present research interests toward their energy harvesting behaviors. This paper theoretically investigates the optimized energy harvesting characteristics of PFGM cantilever beams under harmonic excitation. The electromechanical coupling governing equations are formulated based on Euler–Bernoulli beam theory, and utilizing the Galerkin discretization yields the frequency-response relations of the voltage, current, and power parameters, and the analytical optimal resistance as well. The present theoretical model is validated by comparing with the experimental results in literature, and parametric studies are addressed to discuss the effects of the damping ratio, the inhomogeneous parameter of PFGMs and the electrical resistance on the structural responses. More importantly, the optimized energy harvesting characteristics of PFGM cantilever beams are captured during discussions on the optimal conditions of the frequency-ratio and the electrical resistance. Results reveal that the superiority of PFGM energy harvesters over the conventional piezoelectric laminate ones, basically, lies in the design toward constituent distribution of PFGMs enabling the control over the energy harvesting efficiency. Specifically, provides that both the optimal frequency-ratio and the optimal resistance hold simultaneously, there would be a critical value for the inhomogeneous parameter, which can be utilized to maximize the energy harvesting efficiency for PFGM beams. The present work may support the prospective material gradient design of piezoelectric energy harvesters.
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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".