Design Of A 3D Printed Non-Linear Vibration Energy Harvester Using Electromagnetic Induction.
Bibliographic record
Abstract
New improvements in electronics have resulted in ultra-low power wireless sensors (requiring only a few microwatts of power) optimal for Internet of Things applications. These devices, however, are powered by depletable batteries, which need to be changed, making them less effective. Therefore, vibration energy harvesters have been developed as a source of power for these sensors and to recharge their batteries. The majority of the initial research in this field concentrated on resonant (linear) vibration harvesters. More recently, researchers have started exploring non-linear vibration harvesters as they provide higher power and wider bandwidth. The aim of this paper is to produce a simple 3D printed nonlinear vibration energy harvester, which applies electromagnetic induction and magnetic levitation to transform vertical vibrations into electricity. Some improvements that can better the performance of a non-linear harvester are investigated. Comparisons are made between different topologies based on power, bandwidth and power density. Monostable hardening (double upper magnet double lower magnet topology) showed the best results (+138.1% power density increase and +233.3 maximum power increase). A novel improvement on the power produced by multipole magnets is tested on CST studio; the results showed that the addition of two 1mm thick plates made of steel above and below the moving magnet could improve power by increasing the peak B-field by 9%. Experimentally testing this improvement produced an average voltage increase of 11.73% and power increase of 24.94%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".