Design and Analysis of a Hybrid Segmented Sliding Panel Morphing Skin System
Bibliographic record
Abstract
This thesis presents a rigid morphing skin that is designed to enclose an underlying morphing mechanism and serve as a reliable load bearing protective cover for the enclosed mechanism. The morphing skin, named the passive panel skin system, is a series of geometrically discretized telescopic panels enveloping the underlying morphing mechanism. These panels are connected to the morphing mechanism through a set of linkage systems and each panel can passively reorient with respect to the shape changes of the morphing mechanism. The passive motion of the skin system is governed by constraint equations that correspond to the parallelism and gap distance among adjacent panels. Two main problems have been solved in this thesis: kinematic modeling and force modeling of the proposed system. The kinematic modeling describes the passive panel motion through the simultaneous evaluation of nonlinear constraint equations. The number of the said equations is 3 times the number of panel pairs. The complexity of the kinematic model increases as panel discretization becomes finer. Although the passive panel skin system does not incur additional forces during morphing, there is however an inherent aerodynamic and mechanical gap issue. To solve this, a smart material named shape- memory polymer (SMP) is applied to join the adjacent panels and form a gapless morphing skin. Since SMP is a hyperelastic material, a nonlinear modeling method is applied to model the flexible joints of the gapless panel skin. A force model is developed to address the incurred forces from flexible joints. The model correlates the forces and moments of each joint to the actuation forces of the morphing mechanism. This model helps determine the additional force required from the actuators of the morphing mechanism to achieve the companion motion of the morphing skin as described in the kinematic model of the morphing mechanism. The force model is implemented over a modified workspace that simulates the panel enclosed morphing mechanism. The contributions made in this thesis on kinematic and force model of the passive panel skin system set a foundation for the development of a full morphing system consisting of a morphing mechanism and a morphing skin.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".