Design of a Morphing Airfoil for a Light Unmanned Aerial Vehicle Using High-Fidelity Aerodynamics Shape Optimization
Bibliographic record
Abstract
An in -house high -fidelity aerodynamic shape optimization computer program based on a computational fluid dynamics solver with the Spalart -Allmaras turbulence model and a sequential quadratic programming algorithm is used in order t o obtain a set of optimal airfoils at the different stages of flight of a light unmanned air vehicle. For this study, the airfoil requirements at stall, takeoff run, climb gradient, rate of climb, cruise and loiter conditions are obtained . Then, t he aerody namic shape optimization program is used to obtain the airfoil that has the optimal aerodynamic characteristics at each one of the se stages of flight. Once the optimal airfoils at each stage of flight are obtained, the results are analyzed in order to gain a better understanding of the most efficient initial airfoil configuration and the possible mechanisms that could be used to morph the single element airfoil. The results show that a very thin airfoil could be used as the initial configuration. Furthermor e, a morphing mechanism that controls the camber and leading edge thickness of the airfoil will almost suffice to obtain the optimal airfoil at most operating conditions. Lastly, the use of the optimal airfoils at the different stages of flight significant ly reduce s the installed power requirements, thus enabling a greater flexibility in the mission profile of the unmanned air vehicle.
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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 | 0.001 | 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".