Aerodynamic Shape Optimization of Camber Morphing Airfoil based on Black Widow Optimization
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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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) | 0.002 | 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".