Drag Reduction of Light Weight UAV Wing with Deflectable Surface in Low Reynolds Number Flows
Why this work is in the frame
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Bibliographic record
Abstract
The most effective approach to drag reduction is to concentrate on the components that make up the largest percentage of the overall drag. Small improvements on large quantities can become in fact remarkable aerodynamic improvements. Our experience shows that the use of light material in constructing human-powered airplanes and unmanned-air-vehicles UAVs has a few side effects on the aerodynamic characteristics of their wings. One important side effect is the unwanted deflection on wing shell. It is because of high flexibility and low solidity of the light material, which covers the wing skeleton. The created curvature has direct impact on the separation phenomenon occurred over the wing in low Reynolds number flows. In this work, we numerically simulate the flow over a UAV wing with and without considering the generated deflection on its shell. It is shown that the curvature on the wing surface between two supporting airfoil frames causes total drag coefficient reduction. Indeed, this drag reduction is automatically achieved without benefiting from additional drag-reduction devices and/or drag-reduction considerations. The current investigation has been conducted on a UAV wing with fxmp-160 airfoil section. This airfoil normally provides high lift coefficient in low Reynolds flows because of having suitable camber. The drag of a wing with this airfoil section can be reduced by the proper usage of low weight material as its wing shell providing that the wing shell deflects between its supporting frames during stretching the shell in manufacturing stage.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it