Aero-Structural Optimization of Non-Planar Lifting Surface Configurations
Bibliographic record
Abstract
Non-planar lifting surface aircraft configurations offer potentially significant gains in aerodynamic efficiency by lowering the total induced drag. There are many options for non–planar wing configurations, from winglets and multiwings to box and joined wings. Non–aerodynamic considerations such as structures, weight and stability and control can significantly impact the overall improvements in efficiency.Here, a medium fidelity panel method and equivalent beam finite element model are used to explore the possibilities of non–planar lifting surface configurations taking into account the coupling between aerodynamics and structures. Two main cases, a single discipline aerodynamic optimization and a multidisciplinary aero–structural optimization are investigated. To demonstrate the effect of non–planar configurations, the main lifting surface of a typical commercial aircraft at cruise is optimized. The optimization of the wing configurations is geometrically constrained by a maximum projected span and height. The effect of incorporating parasitic drag in the aerodynamic model is also explored. Due to the complexity of the design space and the presence of multiple local minima, an augmented Lagrange multiplier particle swarm global optimizer is used. The particle swarm algorithm is a global optimization algorithm based on a simplified social model and is closely tied to swarming theory. The aerodynamic optimum solution found for rectangular lifting surfaces is a box wing, as predicted by theory. Allowing for sweep and taper as design variables yields a joined wing as the aerodynamic optimum result. The addition of parasitic drag in the aerodynamic model reduces the size of the non–planar elements in the topology of the aerodynamic optimum solutions. Including structures and the coupling between structures and aerodynamics in the optimization has a profound impact due to the additional weight of non–planar segments. The aero–structural optimal solution found is the C–wing configuration when parasitic drag is neglected and the addition of a winglet to the planar wing when parasitic drag is included.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".