Improving the UAS-S4 Éhecal airfoil high angles-of-attack performance characteristics using a morphing wing approach
Bibliographic record
Abstract
In this paper, a morphing wing approach with a new methodology and its results for the high angles-of-attack optimization of the S4 unmanned aerial system airfoil are described. The boundary layer separation delay, coupled with an increase of the maximum lift coefficient, was achieved using an in-house optimization tool based on the artificial bee colony algorithm, coupled with the Broyden–Fletcher–Goldfarb–Shanno algorithm to provide a final refinement. The obtained results were validated with an advanced, multi-objective, commercially available optimizing tool. The aerodynamic calculations were performed using a two-dimensional linear panel method, coupled with an incompressible boundary layer model and a transition estimation criterion. For very small displacements of the airfoil surface, of less than 2.5 mm, lift coefficient increases of up to 18% together with relevant drag reductions have been achieved, successfully delaying separation for the high angles-of-attack range.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".