Energy transfer between a passing vortex ring and a flexible plate in an ideal quiescent fluid
Bibliographic record
Abstract
Recent advancements in highly deformable smart materials have lead to increasing interest in small-scale energy harvesting research for powering low consumption electronic devices. One such recent experimental study by Goushcha et al. explored energy harvesting from a passing vortex ring by a cantilevered smart material plate oriented parallel to and offset from the path of the ring in an otherwise quiescent fluid. The present study focuses on modeling this experimental study using potential flow to facilitate optimization of the energy extraction from the passing ring to raise the energy harvesting potential of the device. The problem is modeled in two-dimensions with the vortex ring represented as a pair of counter-rotating free vortices. Vortex pair parameters are determined to match the convection speed of the ring in the experiments, as well as the imposed pressure loading on the plate. The plate is approximated as a Kirchhoff-Love plate and represented as a finite length vortex sheet in the fluid domain. The analytical model matches experimental measurements, including the tip displacement, the integrated force along the entire plate length as a function of vortex ring position, and the pressure along the plate. The potential flow solution is employed in a parametric study of the governing dimensionless parameters in an effort to guide the selection of plate properties for optimal energy harvesting performance. Results of the study indicate an optimal set of plate properties for a given vortex ring configuration, in which the time-scale of vortex advection matches that of the plate vibration.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".