Bibliographic record
Abstract
A morphing aircraft adapts to different missions by changing the shape of its wing. With the wingspan change or chord length change, the wing skin will change, which requires the design of the variable wing covering to meet the morphing wing. The core of the variable stiff wing-covering design is splitting the wing surface. By solving the problem, the sliding morphing of the stiff skin is achieved. According to the characteristics of the wing surface, this paper gives two stiff wing-covering segmentation algorithms. First, based on the change of airfoil curve that happened before and after the chord length change, the horizontal noninterference segmentation and morphed method of rigid wing skin is proposed. The airfoil curve is divided into pieces, and the definition of the curve similarity evaluation criterion (that is, ) is presented. The horizontal rigid-surface morphing is achieved, depending on the noninterference folding morphing and the airfoil curve length difference. Second, according to the feature of the column side caused by stretching or shortening of the column, a vertical segmentation method (named “petalized”) of the stiff wing covering for the wingspan change is proposed. By means of the segmentation results of the original column side split through the hierarchical structure and the modular concept, the vertical rigid morphing is achieved. In the last step, two experiments validate the horizontal and vertical algorithms of segmentation and morphing, and they make an analysis of the algorithm complexity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.002 | 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".