Multidisciplinary Design Optimization of a Novel Sandwich Beam-Based Adaptive Tuned Vibration Absorber Featuring Magnetorheological Elastomer
Bibliographic record
Abstract
The present study aims to investigate the dynamic performance and design optimization of a novel magnetorheological elastomer based adaptive tuned vibration absorber (MRE-ATVA). The proposed MRE-ATVA consists of a light-weight sandwich beam treated with an MRE core layer and two electromagnets installed at both free ends. Three different design configurations for electromagnets are proposed. The finite element (FE) model of the proposed MRE-ATVA and magnetic model of the electromagnets are developed and combined to evaluate the frequency range of the absorber under varying magnetic field intensity. The results of the developed model are validated in multiple stages with available analytical and simulation data. A multidisciplinary design optimization strategy has been formulated to maximize the frequency range of the proposed MRE-based ATVA while respecting constraints of weight, size, mechanical stress, and sandwich beam deflection. The optimal solution is obtained and compared for the three proposed ATVA configurations. The optimal ATVA with a U-shaped electromagnet shows more than 40% increase in the natural frequency while having a total mass of 596 g.
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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 | 0.001 | 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".