Dynamic characteristics and control of magnetorheological/electrorheological sandwich structures: A state-of-the-art review
Bibliographic record
Abstract
During past four decades, applications of magnetorheological and electrorheological fluids in adaptive sandwich structures have been widely studied, primarily for the purpose of vibration control. The rapid response time of controllable magnetorheological/electrorheological fluids to an applied magnetic/electric field and reversible variations in their stiffness and damping properties have been the key motivations for adaptive structures applications. This article presents a comprehensive review of the reported studies on applications of magnetorheological/electrorheological fluids for realizing active and semi-active vibration suppression in sandwich structures. The review focuses on methods of characterizing the magnetorheological/electrorheological fluids in the pre-yield region, magnetic/electric field-dependent phenomenological models describing the storage and loss moduli of fluids, experimental and analytical methods developed for vibration analysis of sandwich structures with magnetorheological/electrorheological fluid treatments, analysis of structures with partial magnetorheological/electrorheological fluid treatments and optimal treatment locations, and developments in control strategies for vibration suppression of magnetorheological/electrorheological sandwich structures. The studies on dynamic responses of fully and partially treated magnetorheological/electrorheological-based sandwich beams, plates, shells, and panels are also discussed, including the mathematical modeling methods and associated assumptions, methods of solutions, and experimental methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".