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 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".