A Novel Design and Performance Results of An Electrically Tunable Piezoelectric Vibration Energy Harvester (TPVEH)
Bibliographic record
Abstract
The need for energy harvesters for various applications, including structural health monitoring (SHM) in remote and inaccessible areas, is well established. Energy harvesters can utilize the ambient vibration of the body on which they are mounted to generate energy, thus eliminating the need for an external source of power. One such type of harvester is designed using piezoelectric materials and using a cantilever type set-up. However, the challenge associated with cantilever-based Piezoelectric Vibration Energy Harvesters (PVEH) is that its output power reduces when the ambient vibration frequency deviates from the resonant frequency of the harvester. This calls for a mechanism to tune its resonant frequency to match with the ambient frequency. This article presents an innovative design of an electrically tunable PVEH. The PVEH is integrated with an Ionic Polymer Metal Composite (IPMC) as an actuator that loads the cantilever beam, changing the stiffness of the beam. IPMC utilizes low power, and the authors demonstrate in this paper that a net gain of power can be achieved by this novel design. For the configuration used, it is experimentally proven that a frequency shift from 5.9 Hz to 8 Hz is achieved with three actuation values. Typical power output from the harvester is 52.03 µW when the power spent on actuation is only 0.765 µW. On-going modeling of this system using simulation software is expected to lead to further optimization and prototyping of design.
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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) | 0.002 | 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".