Magnetorheological elastomer-based variable stiffness flexible coupling for vibration isolation
Bibliographic record
Abstract
Rotating mechanical equipment such as pumps and motors can suffer from unattenuated torsional vibration. The causes of such vibration range from imbalance within the rotating system to misalignment, or even operating at frequencies close to the system’s natural frequency. Smart composite materials are materials for which exposure to external stimuli changes their properties. Magnetorheological elastomers (MRE) are smart composite materials whose mechanical properties, such as stiffness, are changed when exposed to a magnetic field. In this article, we report on the fitting of a variable stiffness coupling (VSC) within a shaft to isolate torsional vibration, which can adapt and change its attenuation frequency range. We experimentally tested the VSC concept for isolating torsional vibration. MRE samples with 40% volume fraction were fabricated and manufactured using a 3D mold design and fixed within a coupling in a shaft to investigate the effects of magnetic fields on the torsional rigidity. Impact hammer tests were conducted in combination with an accelerometer to analyze the transmissibility factor. Our results show that the level of vibration decreased when the magnetic field increased. The first natural frequency of the system happened at 26 Hz and moved to 28 Hz when the applied magnetic field increased from 0 to 12.38 mT. The torsional stiffness of the MRE samples increased from from 37.4 to 61.6 N.m/rad when the magnetic field increased from 0 to 12.38 mT. Variation in the torsional damping coefficient fluctuated as the damping effect of MRE was ignored.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".