Hybrid Torsional Vibration Damper Incorporating Conventional Centrifugal Pendulum Vibration Absorber and Magnetorheological Damper
Bibliographic record
Abstract
A hybrid torsional vibration damper is proposed in this study incorporating a conventional Centrifugal Pendulum Vibration Absorber (CPVA) and a Magnetorheological (MR) damper. While CPVAs are simple and reliable passive torsional vibration absorbers, their performance is limited to the designed tune conditions. MR 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. The present research aims at developing a novel hybrid torsional vibration damper combining conventional CPVA with the MR damper capable of suppressing torsional vibration at varying excitation frequencies. This research presents results from analytical investigation of rotor systems integrated with the proposed hybrid torsional vibration damper. The system under investigation consists of a rotor with attached hybrid torsional MR damper subjected to an external harmonic torque. The CPVA has been connected to the cylindrical housing of the MR damper. Different cases have been investigated including: 1- Rotor system without any damper, 2- Rotor system with only CPVA, 3- Rotor system with only torsional MR damper and 4- Rotor system with the hybrid torsional damper. Results for each case have been illustrated and compared against 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".