Practical hysteresis model for magnetorheological dampers
Bibliographic record
Abstract
The magnetorheological dampers have been extensively studied given their benefits such as fail-safe manner, low power requirements, relative fast response, and large force capacity and robustness. To take advantage of this remarkable device, a good model is required to accurately and effectively predict the real-time damping force in magnetorheological damper in order to apply the appropriate control actions to improve the response of the structural system. In this article, a new practical model is proposed to better characterize the hysteresis phenomenon in magnetorheological dampers. The model considers the displacement, velocity, and acceleration excitations as well as the current excitation as input variables and includes a reduced number of constant parameters to be determined. Since the displacement, velocity, and acceleration variables can be obtained in real time from adequate sensors, it is easy and practical to predict the current excitation required to generate the specific hysteresis force. The hysteresis damping forces predicted by the proposed model are validated with those experimentally obtained for different current, amplitude, and frequency of excitation, and a very good correlation has been observed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".