Development of a field dependent Prandtl-Ishlinskii model for magnetorheological elastomers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it