Operational temperature effect on the behavior of magnetorheological dampers
Bibliographic record
Abstract
Magnetorheological (MR) fluid exhibits temperature sensitivity due to a decrease in its viscosity as temperature rises. When MR fluids are used in dampers, this physical phenomenon results in a reduction in the resistive force generated. Temperature changes in MR fluid can occur due to ambient and operational conditions. While some studies have investigated the effect of ambient temperature on the MR dampers, there is a scant literature addressed the impact of operational conditions. This gap in the literature motivates the authors to pursue two primary objectives in this study: first, to experimentally investigate the effects of MR fluid temperature on MR damper performance under different excitation conditions and applied currents; and second, to propose a parametric model including the temperature-related impacts on the MR damper behavior. An RD-8041–1 MR damper was experimentally tested under varying ranges of excitation conditions and applied current. A hyperbolic-tangent-function-based model was refined to consider MR fluid temperature effect in MR damper force prediction. The experimental results show a noticeable reduction in the performance of MR damper during operation, particularly when the damper is subjected to higher velocities and currents. The refined model effectively captures such a damper force decay caused by MR fluid temperature increase and maintains consistent accuracy in the damper force prediction during operation. Furthermore, a comparative case study evaluated the performance of the refined and original hyperbolic-tangent-function-based models in predicting the efficiency of a MR damper in cable vibration control. The simulation results indicate that the ignorance of MR fluid temperature effect may lead to an overestimation of 14.9 % in the control efficiency of a passive MR damper. • Proposed a refined algebraic model to consider MR fluid temperature effect in predicting MR damper force. • MR damper force is found to decrease during operation due to increase of MR fluid temperature. • An increase in excitation amplitude, frequency and current lead to a more considerable reduction in the maximum MR damper force. • The increase in MR fluid temperature manifests a linear relation with the damper piston travel. • A case study showed that ignore MR fluid temperature effect may lead to a 14.9 % overestimation of MR damper control effect.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".