Aerodynamic Shape Optimization of Camber Morphing Airfoil based on Black Widow Optimization
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Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-2575.vid While the conventional control surface-based morphing method is well-developed and widely used on modern aircraft, it is insufficiently effective across the flight envelope. Specifically, aircraft such as UAVs may be expected to perform well at a wide range of flight conditions due to multi-mission flight envelopes. Morphing systems could be a solution to this problem because they allow the aircraft to modify its shape to offer the best aerodynamic performance in any given flight condition. The present study describes a continuous camber morphing airfoil design optimization for the UAS-45 wing using the Modified Akima piecewise cubic Hermite interpolation (Makima) parameterization technique. The design technique is simple and effectively controls the geometry in terms of morphing shape flexibility. Out of the optimization algorithms tested, the BWO is used in this study due to its best performance. The optimizations are performed to maximize the lift-to-drag ratio for cruise and climb flight conditions, respectively and determine the impact of different applied constraints on the accuracy of the optimization. Computational fluid dynamics simulation is used to validate the aerodynamic performance of the camber morphing airfoil. The results show that the optimized configurations outperform the baseline airfoil designs, increasing the lift-to-drag ratio from 48.53 to 86.52 for optimized airfoil relative to a baseline airfoil at cruise flight conditions. It also shows that the lift-to-drag ratio improves at climb flight conditions. Flow field analysis reveals that the continuous morphing method can delay flow separation in some situations.
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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 | 0.001 |
| 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 it