Investigating the Effects of Canard Dihedral Angle on the Wing Span Loading in a Forward-Swept Wing Aircraft at Transonic Speeds at Steady State Conditions Using Computational Fluid Dynamics
Bibliographic record
Abstract
Forward-swept wings were researched and introduced to improve maneuverability, control, and fuel efficiency while reducing drag and they are often used alongside canards, to further enhance their characteristics.In this research, the effects of canard dihedral angles on the wing loading of a forward-swept wing in transonic flow conditions were studied, as the wing loading provides a measure of wing's efficiency (lift/drag).A generic aircraft model from literatures was selected, simulated, and compared to, using CFD software ANSYS/Fluent where the flow equations were solved to calculate the aerodynamic characteristics.The research was carried at two different Mach numbers, 0.6 and 0.9, for five different canard dihedral angles which traverses from below the wing plane to above it, at various flow angles of attack.It was concluded that as the dihedral angle increased, lift increases for the same angle of attack for Mach 0.6 and 0.9 which increases the efficiency of the wing.The wing span loading occur at 10 ° dihedral angle for both Mach speeds, while, at 10 ° anhedral, the lift was minimal due to leading-edge flow separation on the FSW's lower surface.Thus, it is concluded that the canard at positive dihedral angles ensures increased wing span loading efficiency.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".