Bibliographic record
Abstract
This paper presents an aerostructural perspective on the potential benefits of wingletted wings in comparison to planar wings of the same projected span. There is no consensus in the current literature on the efficiency gains possible from winglets. The present paper takes a step further toward understanding the tradeoffs in the design of wingletted wings using high-fidelity numerical optimization based on both purely aerodynamic and fully coupled aerostructural analysis. The high-fidelity analysis in both cases uses the Euler equations to model the flow along with a friction drag estimate based on the surface area. Three configurations are considered: winglet-up, winglet-down, and planar. The results show that downward winglets produce a greater drag reduction than upward winglets for two reasons. First, the downward winglet moves the tip vortex further away from the wing from a purely aerodynamic standpoint. Second, the winglet-down configuration has a higher projected span at the deflected state due to the structural deflections. This indicates that fully coupled high-fidelity aerostructural optimization is required to quantify the benefits of winglets properly. The winglet-down configuration can reduce the total drag by up to 2% at the same weight as the optimal planar counterpart.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".