Bandwidth widening of vibration energy harvesters through a multi-stage design
Bibliographic record
Abstract
A vibration harvester is usually designed to work in resonance responding to source vibration. However, in many cases, this source vibration may occur at a wide range of frequencies. If the harvester has very narrow tuning range, it becomes ineffective when there is a mismatch in the frequencies between source excitation and device resonance. Increasing the bandwidth of vibration harvesters has been an important design objective. We propose a two-stage design to improve of a harvester's performance. In a previous work [J. S. Fernando and Q. Sun, Rev. Sci. Instrum. 84(11), 114704 (2013)], we have demonstrated that use of a two-stage design can increase the power production at a single frequency excitation. In this paper, we will show that a two-stage design can also increase the width of the usable frequency band of the harvester. An optimization routine was used to determine the optimal choice of harvester design parameters with respect to the maximization of an objective function. Experiments were used to verify the electromechanical model as well as the trends predicted by the optimization. Performance comparisons between single- and two-stage harvesters are made through numerical simulation and experiments.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".