Development of a field dependent Prandtl-Ishlinskii model for magnetorheological elastomers
Bibliographic record
Abstract
Magnetorheological elastomers (MREs) offer real-time controllable stiffness and damping properties, and strong hysteresis in the stress-strain responses that depends on magnetic field intensity, strain amplitude and strain rate in a highly nonlinear manner. Prediction of hysteretic stress-strain behavior is essential for effective designs of controllable MRE-based devices. This study presents a stop operator-based Prandtl-Ishlinskii (PI) model for predicting nonlinear hysteresis properties of MREs as functions of the strain amplitude, excitation frequency and magnetic flux density. The stress-strain properties of a MRE fabricated with 40% volume fraction iron particles were experimentally characterized in the shear mode under broad ranges of strain amplitude (2.5–20%), excitation frequency (0.1–50 Hz) and magnetic flux densities (0–450 mT). Subsequently, a stop operator-based classical PI model was formulated considering only 10 hysteresis operators, which required identification of only four parameters. The validity of the classical PI model was assessed using the laboratory-measured data. The proposed classical model is further generalized to enable predictions of MRE dynamic behavior independent of the loading conditions, which would be beneficial for developments in controllable MRE-based adaptive devices. The results demonstrated that the generalized model could accurately characterize nonlinear hysteresis properties of the MRE under the ranges of loading conditions and magnetic field considered.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".