Wing Line Discretization for the Development of a Modular Morphing Wing
Bibliographic record
Abstract
This paper presents a discretization method for the development of a modular morphing wing. The proposed method determines the number of morphing wing modules and their respective spacing required to emulate a known wing shape associated with a particular flight regime/requirement. This method consists of two main steps. The first step is geometry discretization. In this step, curvature and twist distribution from the reference wing quarter chord line are extracted and used to determine the spacing of the discretized wing modules. This is achieved by clustering more, tightly spaced morphing wing modules in areas of large total curvature, and fewer, longer wing modules in areas of small total curvature. By doing so, geometric congruency between the reference and discretized wings is maintained. The second step is for performance evaluation. In this step, an aerodynamic performance index, like the lift-to-drag ratio, for a given flight regime is used to evaluate the effectiveness of each modular morphing wing configuration. Morphing wing modules are sequentially added until an acceptable flight performance is achieved by the discretized wing. The effectiveness of the proposed discretization algorithm is demonstrated through a case study by determining an optimal number of modules for a modular morphing wing.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".