Design optimization and experimental evaluation of a large capacity magnetorheological damper with annular and radial fluid gaps
Bibliographic record
Abstract
This paper presents an optimal design of a large-capacity Magnetorheological (MR) damper suitable for off-road vehicle applications. The damper includes an MR fluid bypass valve with both annular and radial gaps to generate a large damping force and dynamic range. An analytical model of the proposed damper is formulated based on the Bingham plastic model of MR fluids. To establish a relationship between the applied current and magnetic flux density in the MR fluid active regions, an analytical magnetic circuit is formulated and further compared with a magnetic finite element model. The MR valve geometrical parameters are subsequently optimized to maximize the damper dynamic range under specific volume and magnetic field constraints. The optimized MR valve can theoretically generate off-state and on-state damping forces of 1.1 and 7.41 kN, respectively at 12.5 mm/s damper piston velocity. The proposed damper has been also designed to allow a large piston stroke of 180 mm. The proof-of-concept of the optimally designed MR damper was subsequently fabricated and experimentally characterized to investigate its performance and validate the models. The results show that the proposed MR damper is able to provide large damping forces with a high dynamic range under different excitation conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".