Development of a new torsional vibration damper incorporating conventional centrifugal pendulum absorber and magnetorheological damper
Bibliographic record
Abstract
Excessive torsional vibrations in mechanical systems with rotating mechanical components result in noise, excessive stresses, or even fatigue failure if not suppressed or controlled properly. Accordingly, they should be suppressed or controlled immediately to ensure system’s reliability. In this study, a hybrid torsional vibration damper is proposed incorporating a conventional centrifugal pendulum vibration absorber and a magnetorheological damper. While centrifugal pendulum vibration absorbers are simple and reliable than passive torsional vibration absorbers, their performance is limited to the designed tuning conditions. Magnetorheological dampers have recently received considerable attention due to their inherent fail-safe feature, low power requirement, and capability to attenuate vibration under unpredictable environmental conditions. This research aims at developing a novel hybrid torsional vibration damper combining conventional centrifugal pendulum vibration absorber with the magnetorheological rotary damper in which the centrifugal pendulum vibration absorber has been connected to the cylindrical housing of the magnetorheological damper. The analytical model of the rotor system integrated with proposed hybrid torsional damper has been developed. The superior performance of the proposed torsional damper has been then demonstrated by comparing the results with those of rotor system without any damper, rotor system with only centrifugal pendulum vibration absorber, and finally rotor system with only magnetorheological rotary damper.
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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 | 0.001 |
| 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".