Design and Motion Control of Fully Variable Morphing Wings
Bibliographic record
Abstract
The ability to vary the geometry of a wing to adapt to different flight conditions can significantly improve the performance of an aircraft. However, the realization of any morphing concept will typically be accompanied by major challenges. Specifically, the geometrical constraints that are imposed by the shape of the wing and the magnitude of the aerodynamic and inertia loads make the usage of conventional mechanisms inefficient for morphing applications. This paper presents the design of a novel underactuated parallel mechanism, which addresses such concerns. This mechanism, which can be set up in a modular fashion, offers controlled motion in all six spatial degrees of freedom while providing multiple degrees of fault tolerance with only four actuators. The main feature of the design is the usage of active and passive linearly adjustable members to replace the structure of a conventional wing box. These members provide the necessary stiffness and load-bearing capabilities for the wing. With the exception of the skin no additional structure would be required, leading to a relatively light-weight design for the morphing wing. Additionally, an optimal motion control algorithm for minimum energy actuation is proposed based on the kinematics and statics of the mechanism. Finally, the effectiveness of the proposed design and motion control is demonstrated through a simulation followed by the presentation of a multimodule prototype for a wing tip morphing application.
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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 | 0.000 |
| 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".