Control of torsional rotor vibrations using an electrorheological fluid dynamic absorber
Bibliographic record
Abstract
Torsional rotor vibrations are undesirable phenomena which are very difficult to control in rotating systems. A common method for reducing vibrations involves the use of dynamic absorbers. However, if their physical parameters are constant, the frequency range of efficiency of dynamic absorbers is tight, making them unsuitable for systems with variable speeds. The use of smart materials, due to their variable and controllable mechanical properties, may be a powerful tool for increasing the frequency range. Electrorheological (ER) fluids are attractive materials that undergo very fast reversible changes in their rheological properties upon the application of an electric field. In this study, an electrorheological dynamic torsional absorber, called the Smart ER Dynamic Absorber, has been designed in order to reduce torsional rotor vibrations. Under shear mode, the ER absorber can exhibit various torsional damping and stiffness characteristics when an electric field is applied. A nonlinear empirical model of the dynamic behavior of ER materials has been developed. An On-Off control strategy has been applied and absorber efficiency measured. The results show that the ER dynamic absorber exhibits very good performances.
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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 | 0.000 | 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 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".