Development and Relative Assessments of Models for Characterizing the Current Dependent Hysteresis Properties of Magnetorheological Fluid Dampers
Bibliographic record
Abstract
Magnetorheological (MR) dampers exhibit hysteretic and nonlinear force— velocity characteristics, which are strongly dependent upon the nature of the excitation and applied current. A number of reported models for characterizing such hysteretic and nonlinear force—velocity properties are reviewed in view of their applicability for predicting the hysteretic damping force under varying applied current and excitation conditions. It is concluded that the vast majority of these models lack consideration of damping force dependence upon the control current, and the frequency and magnitude of excitation. An independent current function is proposed that could enhance the current-dependent damping force prediction ability of the selected models, when integrated with the hysteretic force function. The parameters of the resulting modified models are identified on the basis of the measured data acquired for a MR-damper under wide ranges of excitation amplitudes and frequencies, and applied currents. These include modified linear biviscous, polynomial, extended Bouc—Wen and generalized sigmoid function models. The validity of modified models and the proposed current function is examined by comparing the model results with measured data under different currents and excitation conditions. The results show that the integration of the proposed current function could significantly enhance the performance of all models in predicting the current-dependent hysteretic damping force. The relative error analyses reveal that modified Bouc—Wen and sigmoid function models can provide reasonably good characterization of nonlinear and hysteretic MR-damping force over a range of current and excitation conditions considered in the study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".