Effects of Wing Flexibility on Vortex Behaviors and Aerodynamic Performance in Various Flapping Flights
Bibliographic record
Abstract
This dissertation presents various studies on vortex structures and aerodynamic characteristics in flapping flexible wings to offer design considerations for flapping micro aerial vehicle (FMAV) developments. A dynamically scaled-up flapping robot, mounted with a6-axis sensor, and a digital particle image velocimetry (DPIV) technique are used to measure aerodynamic force/moment and flow vector-fields during flapping wings in a water tank. Wing models with or without leading edge veins have different thicknesses, providing various wing flexibilities. Studies of flapping wings without veins in two different flapping kinematics are firstly proposed to demonstrate the wing flexibility effect on aerodynamic performance. The flexible wings generally lead to a leading-edge vortex (LEV) generation delay, causing a lift decline. However, the flexible wing in the specific range of flexibility (non-dimensional spanwise flexural stiffness of 35−80) enhances more lift than the rigid and highly flexible wings due to a wider LEV attachment area. The appropriate deformations lead to the attached LEV during the wing reversal. After the stroke reversal, the portion of the attached LEV flows over the leading-edge, causing a downward flux over a new LEV structure near the wingtip. In addition, flexible wings with leading-edge veins had a dynamic camber, causing a poor lift enhancement except for its initial augmentation. Nevertheless, the specific flexible wing (non-dimensional chordwise flexural stiffness of ~1) obtains a higher aerodynamic performance due to reducing the mechanical power requirement to fly. The above LEV attachment is influenced by the downwash from the wingtip vortex (TV). The TV is differed depending on the wingtip shape and the wing deformations, conducting a comparative study of hawkmoth-like and rectangular wings. In a rigid case, the rectangular wing has better aerodynamic performance than the hawkmoth-like wing, whereas in a flexible case, it has not. The flexible hawkmoth-like wing secures a wider LEV region with improved circulations, achieving about twice the increase rate from rigid to flexible wings on lift-drag ratio and a much more lift-power ratio. The results obtained will provide the specific range of wing flexibility and the importance of the wingtip design to allow better aerodynamic performance in FMAV developments with flexible wings.
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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 | 0.000 |
| 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".