Vibration Suppression and Energy Harvesting with a Non-traditional Vibration Absorber: Transient Responses
Bibliographic record
Abstract
This paper focuses on vibration suppression and energy harvesting using a non-traditional vibration absorber referred to as model B. Unlike the traditional vibration absorber, model B has its damper connected between the absorber mass and ground. The apparatus used in the study consists of a cantilever beam attached by a mass at its free end and an electromagnetic energy harvester. The frequency tuning is achieved by varying the beam length while the damping tuning is realized by varying the harvester load resistance. The question addressed is how to achieve the best performance under transient responses. The optimum tuning condition for vibration suppression is based on the Stability Maximization Criterion (SMC). The performance of energy harvesting is measured by the percentage of the harvested energy to the input energy. A computer simulation is conducted. The results validate the optimum parameters derived by the SMC. There is a trade-off between vibration suppression and energy harvesting within the realistic ranges of the frequency tuning ratio and damping ratio. A multi-objective optimization is conducted. The results provide a guideline for obtaining a balanced performance. An experimental study is carried out. The results verify the main findings from the computer simulation. This study shows that the developed apparatus is capable of achieving simultaneous vibration suppression and energy harvesting under transient responses.
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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 | 0.000 | 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".