Analysis and Design Optimization of a Magnetorheological Elastomer-based Vibration Absorber for Maximum Vibration Attenuation of a Main Structure
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-0165.vid Adaptive tuned vibration absorbers (ATVAs) can be effectively utilized in attenuation of unwanted vibrations on a broad range of structures and machinery. The present study, investigates the performance and design optimization of a newly introduced magnetorheological elastomer based adaptive vibration absorber (MRE-AVA) attached to a beam-like host structure. This light-weight vibration absorber consists of a sandwich beam treated with an MRE core layer and two electromagnets installed at both free ends which act as active masses and also provide the required magnetic field for activation of MRE. The host structure considered here is a fixed-fixed aluminum beam clamped at the center to the designed MRE-AVA. First, the mechanical finite element (FE) and magnetic models of the vibration absorber assembly and the host structure are developed. Then, the optimization problem is formulated subjected to mechanical stress and geometrical constraints, with the objective of maximizing the vibration attenuation of the host structure based on driving point mobility analysis. The Sequential Quadratic Programming (SQP) optimization method is utilized to find sub-optimal design candidates for the MRE-AVA. The best design candidate for the vibration absorber provides 99% and 64% decrease in the level of mobility compared to the main structure without absorber around the first and second modes, respectively, while maintaining nearly 16 % adaptive frequency range.
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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 | 0.001 |
| 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 | 0.001 | 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".