Viscoelastic effects on frequency tuning of a dielectric elastomer membrane resonator
Bibliographic record
Abstract
As a recent application of dielectric elastomers (DEs), DE resonators have become an alternative to conventional silicon-based resonators used in MEMS and have attracted much interest from the research community. However, most existing modeling works for the DE resonators ignore the intrinsic viscoelastic effect of the material that may strongly influence their dynamic performance. Based on the finite-deformation viscoelasticity theory for dielectrics, this paper theoretically examines the in-plane oscillation of a DE membrane resonator to demonstrate how the material viscoelasticity affects the actuation and frequency tuning processes of the resonator. From the simulation results, it is concluded that not only the applied voltage can change the natural frequency of the resonator, but also the inelastic deformation contributes to frequency tuning. Due to the viscoelasticity of the material, the electrical loading rate influences the actuation process of the DE resonator, while it has little effect on the final steady frequency tuned by the prescribed voltage within the safety range. With the consideration of the typical failure modes of the resonator and the evolution process of the material, the tunable frequency range and the safe range of the applied voltage of the DE membrane resonator with different dimension parameters are determined in this work, which are found to be dependent on the electrical loading rate. This work is expected to provide a better understanding on the frequency tuning of viscoelastic DE membrane resonators and a guideline for the design of DE devices.
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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 | 0.000 |
| 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 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".