Modelling and analysis of a magneto-rheological damper featuring non-magnetized flow paths in the piston
Bibliographic record
Abstract
The damping characteristics of a magneto-rheological damper featuring non-magnetized flow paths in the piston are analysed using the Eyring constitutive model considering both viscous and minor hydraulic losses. The force–displacement and force–velocity characteristics of the magneto-rheological damper with non-magnetized flow paths were experimentally evaluated under different excitations and magnetic field intensity. Experimental results revealed relatively largerpre-yield-like region, attributed to flows through the non-magnetized paths, which increased with an increase in the coil current. A mathematical model of the damper was subsequently formulated using the Eyring constitutive model considering pressure drop across the piston and viscous effect in addition to the current-dependent friction. The Eyring model parameters were identified as a function of the magnetic field intensity and thus the coil current. For this purpose, a finite element model was formulated to identify a relation between the coil current and the magnetic field intensity. The validity of the proposed model is demonstrated by comparing the model-predicted force–velocity characteristics with the measured data under different applied currents. The model results are also compared with those obtained from the widely reported modified Bouc–Wen model and the Bingham constitutive model. The comparisons showed that the Eyring constitutive model can yield more accurate predictions of the damping properties compared to the Bingham model but similar to those from the modified Bouc–Wen model, while the Bouc–Wen model involves identifications of considerably greater number of parameters. The proposed model provided more accurate prediction of the damping force in the pre-yield region compared to the other models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".