Improvement of vibration energy harvesters mechanical Q-factor through high density proof mass integration
Bibliographic record
Abstract
This paper reports on improvement of the mechanical Q-factor of resonant energy harvesters at ambient pressure via the use of tungsten proof masses by evaluating the impact of the mass size and density on the squeeze film damping. To this end, a simplified model is first proposed to evaluate cantilever beams deflection and the resulting fluid pressure build up between the mass and a near surface. The model, which accounts for simultaneous transverse and rotational motion of very long tip masses as well as for 2D fluid flow in the gap, is used to extract a scaling law for the device fluidic Q-factor Q f . This law states that Q f can be improved by either increasing the linear mass density of the tip mass or by reducing the side lengths compared to the gap height. The first approach is validated experimentally by adding a tungsten proof mass on a silicon based device and observing an improvement of the Q-factor by 103%, going from 430 to 871, while the resonance frequency drops from 457 to 127 Hz. In terms of fluidic Q-factor, this represents an increase from 562 to 1673. These results successfully demonstrate the benefits of integrating a tungsten mass to reduce the fluid losses while potentially reducing the device footprint.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".