Evaluation and validation of a multiphysics finite element model for a piezoelectric energy harvester
Bibliographic record
Abstract
This study explores the applicability of the multiphysics finite element method to model a piezoelectric energy harvester. The piezoelectric energy harvester under consideration consists of a stainless-steel cantilever beam attached to a piezoelectric ceramic patch. Two configurations were considered: one without a proof mass and one with a proof mass. COMSOL Multiphysics software is used to simultaneously model three physics: solid mechanics, electrostatics, and electrical circuit physics. Several key relationships were investigated to predict the behaviour of the piezoelectric energy harvester. The effects of the electrical load resistance and proof mass on the performance of a piezoelectric energy harvester were evaluated. Experimental testing was conducted to validate the results obtained using the finite element model. Overall, the results from the finite element model closely matched those from the experimental testing. It was found that increasing the load resistance of the piezoelectric energy harvester caused an increase in voltage across the load resistor, and matching the impedance yielded the maximum power output. Increasing the proof mass reduces the fundamental frequency, which results in an increase in the displacement transmissibility and impedance-matched resistance. The study shows that the multiphysics finite element method is effective for modelling 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".