Methods to improve harvested energy and conversion efficiency of viscoelastic dielectric elastomer generators
Bibliographic record
Abstract
As a new transduction technology, dielectric elastomer generators (DEGs) are capable of converting mechanical energy from diverse sources into electrical energy. However, their energy harvesting performance is strongly affected by the material viscoelasticity. Based on the finite-deformation viscoelasticity theory and the nonlinear coupled field theory for dielectric elastomers, this work presents a theoretical framework to model the performance of DEGs. Motivated by the recent experiments of DEGs with a triangular harvesting scheme, we propose a method to optimize the harvesting cycle, which could significantly improve the conversion efficiency of viscoelastic DEGs. From our simulation results, choosing a higher voltage power source appears to be an effective way to improve the performance of DEGs. In addition, optimizing the period of the discharging process of DEG can markedly increase its efficiency. Also, we have uncovered that the triangular harvesting scheme for DEGs, which is expected to harvest energy close to the maximum achievable energy, could be actually realized by choosing dielectric elastomers with a higher fraction of time-independent polymer networks. The theoretical framework and simulation results presented in this work are expected to benefit the optimal design of DEGs for different 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 | 0.000 |
| 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".