A Single Input-Multiple Output Curve Adaptive Linkage Array
Bibliographic record
Abstract
Development of morphing systems is a fast evolving area of research with broad applications, including morphing aircraft. Classified based on the magnitude of the motion, there are generally two scales of morphing: local (small scale; e.g. airfoil morphing), and global (large scale; e.g. wing morphing), both requiring adaptive structures to provide morphing motion while maintaining structural rigidity. In this paper, a new design is presented for local morphing, inspired by the notion of minimal actuation effort. Based on the concept of multi-loop linkages, this design allows a morphing curve, represented by a series of points, to take up three distinct shapes, with a single actuation input. The underlying design is based on a network of four-bar linkages connected together to form a multi-loop linkage, referred to as the Curve Adaptive Linkage Array (CALA). A three-step method is developed and presented here to find the geometric dimensions of the CALA. Furthermore, a case-study for an airfoil morphing application is presented and solved using the proposed method. The presented method provides a means to reduce the number of actuators needed for shape morphing, and is generally applicable to any shape 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".