Electromechanical modelling of l-shaped bending-torsion piezoelectric energy harvesting systems
Bibliographic record
Abstract
• Analytical modelling is performed to study and compare the performances of three L-shaped PEHs H 1 , H 2, and H 3 . • The effect of exploiting the face shear piezoelectric coefficient (d36) is shown. • The analytical results are validated using numerical and experimental data. • Effective design is performed for the PEHs H 2 and H 3 . Piezoelectric energy harvesting (PEH) has become an important concept in recent years, aiming to harvest energy from various mechanical vibrations. However, a significant gap has been noticed in the multimodal and multidirectional analytics of PEHs. In this context, this paper proposes an innovative approach to introduce a novel multimodal and multidirectional PEH design, which exploits both the face extension mode (d31) and the face shear mode (d36), to target low-frequency environments. The study investigates three bending-torsion L-shaped bimorph PEHs, namely H 1 , H 2 , and H 3 . To evaluate the performance of the proposed PEHs, a distributed parameter model is developed based on the assumptions of the Euler-Bernoulli beam. The validity of the model is confirmed by analytical, numerical, and experimental results from previous work. The exploitation of the d36 mode enhances the power output of H 1 , H 2 , and H 3 by 67.95%, 43.66%, and 31.83 respectively. The three PEHs performances are compared quantitatively and qualitatively, and H 1 is found to be the most efficient among them. The PEH H 1 requires a single excitation to perform the coupled bending-torsion motions, resulting in an outstanding average power output of 54.66 µW, which outperforms the other two PEHs by tenfold. The use of the d36 modes to expand the frequency bandwidth and improve the power output presents an excellent innovative and practical value for piezoelectric energy harvesting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".