Investigation on the performance of a viscoelastic dielectric elastomer membrane generator
Bibliographic record
Abstract
Dielectric elastomer generators (DEGs), as a recent transduction technology, harvest electrical energy by scavenging mechanical energy from diverse sources. Their performance is affected by various material properties and failure modes of the dielectric elastomers. This work presents a theoretical analysis on the performance of a dielectric elastomer membrane generator under equi-biaxial loading conditions. By comparing our simulation results with the experimental observations existing in the literature, this work considers the fatigue life of DE-based devices under cyclic loading for the first time. From the simulation results, it is concluded that the efficiency of the DEG can be improved by raising the deforming rate and the prescribed maximum stretch ratio, and applying an appropriate bias voltage. However, the fatigue life expectancy compromises the efficiency improvement of the DEG. With the consideration of the fatigue life, applying an appropriate bias voltage appears to be a more desirable way to improve the DEG performance. The general framework developed in this work is expected to provide an increased understanding on the energy harvesting mechanisms of the DEGs and benefit their optimal design.
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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 | 0.001 |
| 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".