A Wideband Piezoelectric Energy Harvester Design by Using Multiple Non-Uniform Bimorphs
Bibliographic record
Abstract
This paper presents an analytical approach for the development of a new wideband piezoelectric energy harvesting system. The proposed model is based on Adomian decomposition method to derive the dynamic response of the general non-uniform smart structures under external environmental excitations over a wide frequency domain efficiently harvesting the subsequent vibrational energy. The steady-state response of a nonlinearly tapered piezoelectric harvester subjected to harmonic base motion is obtained, and the higher potential electromechanical outputs compared with traditional uniform harvester are analytically derived. Afterward, a group of nonlinearly tapered cantilevers with the same volume and length but different taper ratios and surface bonded piezoelectric layers are assembled together in order to build a broadband piezoelectric energy harvester. Through numerical studies, it is proven that with the proposed non-uniform configuration, the new energy harvester design can function effectively and efficiently with high voltage output over a wide frequency range. The designed wideband harvester can automatically activate one of the non-uniform bimorphs to resonate at particular ambient vibration frequencies and eventually reach the maximum electromechanical output. Based on the proposed theoretical model, an optimum structural design for the wideband piezoelectric energy harvester in the required operational frequency range can be efficiently achieved.
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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 | 0.001 |
| Open science | 0.001 | 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".