Numerical optimization of the S4 Éhecatl UAS airfoil using a morphing wing approach
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Bibliographic record
Abstract
In this paper, we describe the new methodology and the results obtained for multiple flight conditions optimization of the airfoil of the S4 unmanned aerial system, using a morphing wing approach. The goal of reducing the airfoil drag coefficient over a broad range of speeds and angles of attack 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. The obtained results were validated with an advanced, multi-objective, commercially available optimizing tool. The aerodynamic calculations were performed using a 2D linear panel method, coupled with an incompressible boundary layer model and a transition estimation criterion, to provide accurate estimations of the airfoil drag coefficient. For very small displacements of the airfoil surface, less than 2.5 mm, drag reductions of up to 14% have been achieved for a wide range of different flight conditions.
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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) | 0.000 | 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) | 0.000 | 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