Fixed and rotary wing transonic aerodynamic improvement via surface-based trapped vortex generators
Bibliographic record
Abstract
A novel passive flow control concept for transonic flows over airfoils is proposed and examined via computational fluid dynamics. The control concept is based on the local modification of the airfoil's geometry. It aims to reduce drag or to increase lift without deteriorating the original lift and/or drag characteristics of the airfoil, respectively. Such flow control technique could be beneficial for improving the range or endurance of transonic aircraft or for mitigating the negative effects of transonic flow on the advancing blades of helicopter rotors. To explore the feasibility of the concept, two-dimensional computational fluid dynamics simulations of a NACA 0012 airfoil exposed to a freestream of Mach 0.7 and Re = 9 × 10 6 as well as of a NASA SC(3)−0712(B) supercritical airfoil exposed to a freestream of Mach 0.78 and Re = 30 × 10 6 were conducted. The baseline airfoil simulations were carefully verified and validated, showing excellent agreement with wind tunnel data. Then, 32 various local geometry modifications were proposed and systematically examined, all functioning as a trapped-vortex generator. The surface modifications were examined on both the upper and lower surfaces of the airfoils. The upper surface modifications demonstrated remarkable ability to reduce the strength of the shockwave on the upper surface of the airfoil with only a small penalty in lift. On the other hand, the lower surface modifications could significantly increase the lift-to-drag ratio for the full range of the investigated angles of attack, when compared to the baseline airfoil.
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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".