Aerodynamic Design and Performance Optimization of Camber Adaptive Winglet for the UAS-S45
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-1041.vid Winglets are well-known devices that increase aircraft fuel efficiency by allowing for high lift-to-drag ratios and reduced induced drag. Morphing structures can be implemented in specific parts of the aircraft to improve its flight performance and maneuverability. The present study describes the design optimization of a camber adaptive winglet for the UAS-45 wing using the Modified Akima piecewise cubic Hermite interpolation (Makima) parameterization technique. This design technique is simple and effectively controls the winglet geometry in terms of morphing shape flexibility. The Particle Swarm Optimization (PSO) algorithm coupled with the Pattern Search is used in this study for optimizing the winglet. By employing the Vortex Lattice Method (VLM) in MATLAB to calculate the aerodynamic properties of the winglet geometry, and a range of airfoil sections can be created and evaluated under various flight conditions to determine the optimal shapes. These optimizations are performed to minimize the drag for climb flight condition and maximize the endurance for cruise flight conditions respectively, and to determine the impact of different constraints on the accuracy of the optimization. The lift-induced drag was reduced for both the climb and cruise flight conditions by using the optimization framework to define the camber section of a winglet. This research discusses the optimization technique and compares winglet geometries, demonstrating that changing the winglet geometry in flight can enhance aircraft performance while lowering drag, therefore the fuel consumption.
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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".