A novel magnetorheological elastomer-based adaptive tuned vibration absorber: design, analysis and experimental characterization
Bibliographic record
Abstract
Abstract The present study aims at the development of a novel semi-active adaptive tuned vibration absorber (SATVA) capable of tuning its natural frequency adaptively in low frequency range. The absorber consists of a multilayer sandwich beam featuring magnetorheological elastomer (MRE) and integrated U-shaped electromagnets which are attached at the top and bottom layers of the sandwich beam. Electromagnets are designed to provide the required magnetic field to alter the stiffness of the MRE layers while also acting as the active mass of the absorber. Based on the characterization of the shear and loss modulus of the fabricated MRE samples, the finite element (FE) model of the proposed SATVA has been developed to analyze the absorber to meet the design requirements and also to evaluate its dynamic performance. The proposed SATVA is then fabricated and experimental set-ups are designed to validate the electromagnet and FE models. The frequency response function of the proposed SATVA is then investigated under different levels of the applied current to the electromagnets. It has been shown that good agreement exists between simulation and FE results. A frequency-shift of approximately 9% was achieved while maintaining a reasonable factor of safety for material constraints. Finally, using the validated FE mode, a parametric study has been conducted to investigate the effect of different design parameters.
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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 | 0.001 | 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".