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Record W4386215869 · doi:10.32920/24043416.v1

Design and Analysis of a Hybrid Segmented Sliding Panel Morphing Skin System

2023· preprint· en· W4386215869 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicStructural Analysis and Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMorphingDiscretizationKinematicsMechanism (biology)Nonlinear systemComputer scienceMathematicsMathematical analysisPhysicsArtificial intelligenceClassical mechanics

Abstract

fetched live from OpenAlex

<p>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.</p> <p>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.</p> <p>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.</p>

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.783
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.221
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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