Modelling of a Piezoelectric Actuator under Large Deformation for Smart Structure Applications
Bibliographic record
Abstract
The current paper presents our recent progress in the analysis and simulation of the electromechanical response of piezoelectric actuators. Of particular interest is the general behavior of thin-sheet actuators. As fundamental elements, this type of actuator plays an important role in the design of smart structures, which can be programed and/or reconfigured in service. Although the linear behavior of this type of actuator has been extensively investigated, there has been a lack of study of their nonlinear electromechanical behavior under large deformation. The current theoretical analysis evaluates the effects of nonlinearity on the performance of a piezoelectric thin-sheet actuator under coupled electromechanical loads. The actuator is modelled as a Euler-Bernoulli beam with consideration of the electromechanical coupling effect. The response of the actuator is dominated by the load transfer between the actuator and the host structure, and as a result, the formulation of the problem is established by developing nonlinear integral equations to determine the interfacial stresses. The coupled nonlinear behavior of the actuator under various mechanical and electrical loads is then evaluated. The current study provides new valuable results describing the nonlinear behavior of piezoelectric actuators and enhances the understanding of actuator performance in smart structure applications.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".