Innovative electromagnetic vibration energy harvester with free-rotating mass for passive resonant frequency tuning
Bibliographic record
Abstract
Vibration energy harvesters (VEHs) can be used to power wireless electronic devices by converting mechanical energy into electrical energy. However, these harvesters are generally resonant structures with narrow bandwidth, posing challenges with respect to the operating frequency range. This paper presents a novel electromagnetic VEH with a structure that passively tunes its resonant frequency. The proposed design is primarily composed of a flat spring and a freely rotating mass. The design was simulated and tested experimentally. Tests showed that the mass can rotate towards the resonant position, dynamically changing the resonant frequency of the structure, to match the vibration frequency. The VEH demonstrated a resonant frequency range of 10 Hz, from 60 to 70 Hz, and when compared to the same structure with a fixed mass, it showed a 90 % improvement in bandwidth, from 12 to 22 Hz. These results show that passive resonant frequency tuning can significantly improve the operating frequency range of VEHs for practical use. The normalized power density of the VEH was 1.64 kgs/m 3 in vibrations of 60 Hz and 1 g, demonstrating that it is capable of powering wireless electronics. • First passive tuning of resonant frequency in an electromagnetic vibration harvester with a free-rotating mass. • A passive resonant frequency range of 10 Hz is demonstrated. • The bandwidth is improved by 300 %, from 5 Hz to 20 Hz, when compared to a fixed mass. • A normalized power density of 1.64 kgs/m 3 is demonstrated in 60 Hz and 1 g vibrations.
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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 | 0.001 | 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".