Dynamic analysis of a tunable viscoelastic dielectric elastomer oscillator under external excitation
Bibliographic record
Abstract
Abstract As a category of soft electroactive materials, dielectric elastomers (DEs) show great potential for the development of tunable oscillators and resonators for actuating and sensing purposes. However, the dynamic performance of these DE-based vibration devices could be very susceptible to external environment (external loads and excitations) and material viscoelasticity of the DEs. Based on the finite-deformation viscoelasticity theory, this work first investigates the frequency tuning process of a viscoelastic DE membrane oscillator. A comparison of the frequency tuning process and the tunable frequency range between a viscoelastic and a purely elastic DE oscillator is presented. Moreover, particular considerations have been given to the nonlinear response of the oscillator to external harmonic excitation. It is found that the displacement transmissibility of the oscillator can also be actively tuned by changing the static voltage applied to the DE membrane. Under harmonic excitation, various vibration patterns of the oscillator could be actively achieved with the application of both static and alternating electric voltage. Simulation results in this work demonstrate that the material viscoelasticity has a significant effect on the electromechanical coupling and the dynamic performance of the DE-based vibration devices.
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.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".