The effect of stiffness and geometric parameters on the nonlinear aeroelastic performance of high aspect ratio wings
Bibliographic record
Abstract
The increase in wing aspect ratio is gaining interest among aircraft designers in conventional and joined-wing configurations due to the higher lift-to-drag ratios and longer ranges. However, current transport aircraft have relatively small aspect ratios due their increased structural stiffness. The more flexible the wing is more prone to higher deflections under the same operating condition, which may result in a geometrical nonlinear behavior. This nonlinear effect can lead to the occurrence of aeroelastic instabilities such as flutter sooner than in an equivalent stiffer wing. In this work, the effect of important stiffness (inertia ratio and torsional stiffness) and geometric (sweep and dihedral angles) design parameters on aeroelastic performance of a rectangular high aspect ratio wing model is assessed. The torsional stiffness was observed to present a higher influence on the flutter speed than the inertia ratio. Here, the decrease of the inertia ratio and the increase of the torsional stiffness results in higher flutter and divergence speeds. With respect to the geometric parameters, it was observed that neither the sweep angle nor the dihedral angle variations caused a substantial influence on the flutter speed, which is mainly supported by the resulting smaller variations in torsion and bending stiffness due to the geometric changes.
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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 | 0.000 | 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".