Semi-Active Control of Torsional Vibrations Using a New Hybrid Torsional Damper
Bibliographic record
Abstract
Torsional vibration is an important issue in many industrial applications with rotating mechanical components. Excessive torsional vibrations may result in noise, excessive stresses or even fatigue failure if not controlled expeditiously. The present research aims at developing a novel hybrid semi-active torsional vibration damper incorporating a conventional Centrifugal Pendulum Vibration Absorber (CPVA) and a Magnetorheological (MR) damper capable of suppressing torsional vibration under varying excitation frequencies. Two different strategies were carried out for controlling damping torque of the MR damper. First, the passive control approach of the MR damper is implemented in two cases, 1) When the applied current is set to zero, 2) When it is set to a constant value. The system response is investigated at resonance condition, when the excitation frequency coincides with the natural frequency of the system and torsional vibration response of the system is illustrated in each case. In the second approach, the semi-active Skyhook control algorithm with variable applied current is implemented for improving the performance of the hybrid damper. Torsional response of the system is illustrated in each case and compared with one another.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".