Adaptive Airfoils for Drag Reduction at Transonic Speeds
Bibliographic record
Abstract
Adaptive airfoils and wings can provide superior performance at the expense of increased cost and complexity. In this paper, an aerodynamic optimization algorithm is used to assess an adaptive airfoil concept for drag reduction at transonic speeds. The objective is to quantify both the improvements in drag that can be achieved and the magnitude of the shape changes needed. In an initial study, a baseline airfoil is designed to produce low drag at a fixed lift coefficient over a range of Mach numbers. This airfoil is compared with a sequence of nine airfoils, each designed to be optimal at a single operating point in the Mach number range. Shape changes of less than 2 % chord lead to drag reductions of 4-6% over a range of Mach numbers from 0.68 to 0.76. If the shape changes are restricted to the upper surface only, then changes of less than 1 % chord lead to drag reduction of 3-5%. In a second study, a baseline airfoil is designed based on a multi-point optimization over eighteen operating points, including dive and low-speed off-design requirements. Adaptive airfoils are designed through single-point optimization for the operating points corresponding to cruise conditions, producing drag reductions ranging from 9.7 to 16.7 % with shape changes on the order of a few percent chord. I.
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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".