Experimental and computational investigations of flapping wings for Nano-air-vehicles
Bibliographic record
Abstract
This paper presents the finalized results of a recent project which investigated the aeromechanical aspects of aerodynamic force generation by making use of flapping wings. Flapping-wing experiments using small wings have some unique challenges posed by the low force level (∼1 N) and the cyclic wing motion. A tailored experimental water tunnel facility was developed for flapping wings operating at high reduced frequency with a complex two-dimensional and a three-dimensional motion profile. The experimental capability is demonstrated by the test cases of two-dimensional and three-dimensional flapping wings, designed according to a proposed notional nano-air-vehicle at a hovering condition. The features of the water tunnel, the geometric and kinematic parameters of the airfoils/wings, and the setups of the motion rigs for each test case are described. Measured forces and particle image velocimetry data are analyzed and cross-checked with the numerical results obtained from a code developed in-house. The comparisons of the experimental and numerical results show that the established experimental approach obtained a quantitatively reliable solution for the development of flapping wings and can serve for numerical validation of engineering tool developments. The investigation reveals that the kinematics of a rigid airfoil or wing is the dominant influence in the generation of aerodynamic forces, while the cross-section profile plays a secondary role. An asymmetric-wake-in-time is found behind the single airfoils and wings, which contributes to an asymmetry behavior of the resulting aerodynamic forces. In addition to the findings of single airfoils and wings, further analyses of the numerical and experimental results confirm that wing-wing interaction through the clap-fling mechanism can intensify the generation of the thrust force while accompanied by a small reduction in the overall propulsion efficiency.
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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 | 0.000 |
| 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".