A Hybrid Analytical/Numerical Model of Piezoelectric Stack Actuators Using a Macroscopic Nonlinear Theory of Ferroelectricity and a Preisach Model of Hysteresis
Bibliographic record
Abstract
Hysteretic behavior of polarized piezoelectric materials is of importance in the context of high power electro-mechanical transduction. The main goal of this article is to combine the Tiersten theory of electroelasticity and a Preisach model of hysteresis to model a simple force loaded piezoelectric actuator. The proposed theory of piezo-ferro-elasticity extends the derivation of Tiersten and Huang to the case of an arbitrary polarization direction in the ferroelectric domain. The general model is subsequently reduced to a mono-dimensional stack actuator under low operating voltage; therefore, the modeling focuses on minor loop modeling around the initial state of polarization. The model of hysteresis between polarization and electric field proposed here takes into account the nonlocal (macroscopic) memory of a piezoelectric ceramic by the use of a Preisach model. The Preisach model of hysteresis and the set of new ferro-electro-elastic constants that account for the irreversible relation between electric field and polarization are identified through experiments. The nonlinear model successfully reproduces the nonlinear dependence of generated displacements and forces as a function of applied electric field. Possible applications of this work involve nonlinear actuator behavior in the active control of vibration.
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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) | 0.000 | 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 | 0.001 |
| 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".