Optimal Configuration Design for the Variable Geometry Wing-Box
Bibliographic record
Abstract
In wing morphing, it is desirable to have a system that acts both as a load-bearing structure and a morphing mechanism, without any distinction between the two. When dealing with a rather large design space, designing for this requirement would be less challenging because the constituting structural elements could be topologically placed in a fashion to enhance the static characteristics of the system, without sacrificing its kinematic abilities. However, in the case of an aircraft wing, where the design space is highly restrictive, conventional approaches typically yield inefficient designs from a static perspective. Such restrictions have served as inspirations for the proposed concept of a reconfigurable system, which is able to alter its kinematic and static characteristics to act both as a mechanism and a high-stiffness structure. The optimal configuration design of this system, referred to as the variable geometry wing-box is discussed in this paper. The optimal configuration design problem is posed in two parts: 1) the optimal limb configuration, and 2) the optimal topological configuration. The former seeks the optimal design of the kinematic joints and links, while the latter seeks the minimal compliance solution to their placement within the design space. In addition to the static and kinematic criteria required for reconfigurability, practical design considerations such as fail-safe requirements and design for minimal aeroelastic impact are included as constraints in the optimization process. In the end, the optimal configuration for the variable geometry wing-box is presented, and its rigidity is validated using a reconfigurable prototype.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".