Aerodynamic performance improvement of the UAS-S4 Éhecatl morphing airfoil using novel optimization techniques
Bibliographic record
Abstract
In this paper, we present a morphing wing concept of the airfoil of the S4 unmanned aerial system, the new optimization methodology and the results obtained for multiple flight conditions. The reduction of the airfoil drag coefficient has been achieved using an in-house optimization tool based on the relatively new Artificial Bee Colony algorithm, coupled with the Broyden–Fletcher–Goldfarb–Shanno algorithm to provide a final refinement of the solution. A broad range of speeds and angles of attack have been studied. An advanced, multi-objective, commercially available optimizing tool was used to validate the proposed optimization strategy and the obtained results. The aerodynamic calculations were performed using the XFOIL solver, a two-dimensional linear panel method, coupled with an incompressible boundary layer model and a transition estimation criterion, to provide accurate estimations of the airfoil drag coefficient. Drag reductions of up to 14% have been achieved for a wide range of different flight conditions, using very small displacements of the airfoil surface, of only 2.5 mm.
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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".