Development and experimental characterization of a large-capacity magnetorheological damper with annular-radial gap
Bibliographic record
Abstract
Abstract Magnetorheological (MR) dampers with bypass arrangements and combined annular-radial fluid flow channels have shown superior performance compared to conventional MR dampers with single annular/radial fluid flow gaps. Achieving a higher controllable dynamic force range with low off-state but high on-state damping force is yet a significant challenge for developing MR dampers for high payload ground vehicle suspensions. This paper presents the conceptual design, fabrication, and experimental characterization of a mid-sized large-capacity MR damper equipped with a compact annular-radial MR fluid bypass valve. Extensive experimental tests were conducted to investigate the dynamic characteristics of the proposed MR damper considering wide ranges of excitation frequency, loading amplitude, and electrical current. The equivalent viscous damping and the dynamic range were calculated as functions of loading conditions considered. The proposed damper initially realized the maximum dynamic range and damping force of 2.3 and 5.54 kN, respectively. With MR valve design modifications, the maximum dynamic range and damping force were substantially increased, reaching 5.06 and 6.61 kN, respectively. The effectiveness of the proposed MR damper was subsequently identified by comparing its dynamic range with other conventional MR dampers in previous studies. The results confirmed the superior performance of the proposed MR damper and its potential application for highly adaptive suspension systems for off-road wheeled and tracked vehicles.
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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 | 0.000 | 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".