Drag Reduction of Light Weight UAV Wing with Deflectable Surface in Low Reynolds Number Flows
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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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 | 0.001 | 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".