Simultaneous vibration suppression and energy harvesting with a non-traditional vibration absorber
Bibliographic record
Abstract
This article investigates the effectiveness of using a non-traditional vibration absorber for the purpose of simultaneous vibration suppression and energy harvesting. Unlike the commonly used vibration absorber in which the absorber damper is attached to the primary mass, the non-traditional vibration absorber under study has its damper connected between the absorber mass and the ground. In this article, an apparatus of such a configuration is developed. It consists of a primary system subjected to a ground excitation and the non-traditional vibration absorber whose frequency and damping can be tuned. An electro-magnetic device serves as both the absorber damper and the energy harvester. The optimum parameters of the vibration absorber are derived based on the classical “fixed-points” theory. The mechanism of the electro-magnetic damper is developed and its energy harvesting performance is investigated. The results are validated using both computer simulation and experiment. The study shows that when the vibration absorber is optimally tuned with respect to the frequency tuning ratio and load resistance, the frequency response function of the primary mass can be made near flat in a wider frequency band. It also demonstrates that the dual purpose of vibration suppression and energy harvesting can be achieved with the proposed absorber.
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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) | 0.001 | 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".