The effect of biologically-inspired, passive, leading-edge tubercles on static and flapping wing flight
Bibliographic record
Abstract
Leading-edge tubercles, inspired by humpback whale pectoral flippers, were used in an attempt to improve static and flapping wing performance. Wings with either sinusoidal tubercles or discrete leading-edge tubercles were tested. The best wing used eighteen discrete tubercles of amplitude 4.54% chord, increasing stall angle by 18%, CLmax by 7%, and decreasing drag by 8.4%. Decreased drag was seen only past the stall angle of the baseline case, suggesting that this was due mostly to stall delay. Computer simulations showed that the tubercles delayed stall by inducing boundary layer mixing, using two counter-rotating, stream-wise vortices. The discrete-tubercles on the flapping wing decreased thrust at alpha = 0° without affecting lift. However, at 6° AOA the tubercles reduced drag, but also decreased lift. The tubercles seemed only to benefit the performance of static wings, but this performance increase did not justify the added construction costs associated with their geometric complexity.
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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".