A lifting line model to investigate the influence of tip feathers on wing performance
Bibliographic record
Abstract
Bird wings have been studied as prototypes for wing design since the beginning of aviation. Although wing tip slots, i.e. wings with distinct gaps between the tip feathers (primaries), are very common in many birds, only a few studies have been conducted on the benefits of tip feathers on the wing's performance, and the aerodynamics behind tip feathers remains to be understood. Consequently most aircraft do not yet copy this feature. To close this knowledge gap an extended lifting line model was created to calculate the lift distribution and drag of wings with tip feathers. With this model, is was easily possible to combine several lifting surfaces into various different birdwing-like configurations. By including viscous drag effects, good agreement with an experimental tip slotted reference case was achieved. Implemented in C++ this model resulted in computation times of less than one minute per wing configuration on a standard notebook computer. Thus it was possible to analyse the performance of over 100 different wing configurations with and without tip feathers. While generally an increase in wing efficiency was obtained by splitting a wing tip into distinct, feather-like winglets, the best performance was generally found when spreading more feathers over a larger dihedral angle out of the wing plane. However, as the results were very sensitive to the precise geometry of the feather fan (especially feather twist) a careless set-up could just as easily degrade performance. Hence a detailed optimization is recommended to realize the full benefits by simultaneously optimizing feather sweep, twist and dihedral angles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".