Design optimization and experimental characterization of a rotary magneto-rheological fluid damper to control torsional vibration
Bibliographic record
Abstract
Abstract This paper aims at optimum design formulation of a rotary disk-type magneto-rheological (MR) fluid damper to increase its torsional vibration control performance. The objective is to maximize the torsional damping torque for a given volume, geometric and inertia constraints. The damping torque has been derived based on Bingham plastic model for a commercial MR fluid provided by Lord corporation. As MR fluid’s yield strength directly depends on the applied magnetic field intensity, an analytical magnetic circuit analysis has been conducted to approximately evaluate the magnetic field intensity in the MR fluid gap. A finite element model of the rotary MR damper has also been developed to evaluate the magnetic field distribution. A formal design optimization problem has then been formulated to maximize the dynamic range for a given volume under geometric, inertia and torque ratio constraints. Genetic algorithm combined with sequential quadratic programming method has been utilized to accurately capture the global optimum solution. Finally, a proof-of-concept of the optimal design has been manufactured and then tested experimentally to investigate the generated damping torque under different current excitation and also to validate the model and optimization strategy.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".