Impact of phase and spacing on two flapping plates in a clap-and-flingconfiguration
Bibliographic record
Abstract
Flapping wings are of great interest for the miniaturization of flying devices such as drones. Previous studies have shown the benefits of using the clap-and-fling phenomenon in such applications operating in low Reynolds flows. Indeed, it has been demonstrated that aerodynamic forces generation produced by this mechanism is more efficient at smaller scales. As such, it has been adopted by small birds and insects as their way of flight through natural selection. This mechanism consists of two flapping wings that get close to each other during the flight motion and is responsible for generating an important level of propulsive force. In order to better understand the interaction between the two wings and the surrounding flow, laminar numerical simulations are performed by using the overset mesh technique to properly follow the prescribed motion of two identical flat plates. These plates undergo a combined pitching and heaving motion, with one plate having a movement that mirrors the other. The heaving and pitching components of the motion are performed at the same frequency, but with a phase shift between them. This phase shift as well as the minimum spacing between the plates during one period of motion are the main parameters investigated in this study. By optimizing these two parameters, the results obtained from the simulations show that a high level of propulsion is achieved with propulsive efficiencies as high as 52.25 %. This represents an increase of 11.78 % over the case of a single plate which is not subjected to the clap-and-fling phenomenon.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".