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Record W4237159913 · doi:10.32920/ryerson.14665278.v1

Conceptual Design Of A Modular Morphing Wing

2021· preprint· en· W4237159913 on OpenAlexaff
Allan D. Finistauri

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMorphingModular designKinematicsTrussWingModularity (biology)Computer scienceMechanism (biology)Parametric statisticsEngineeringStructural engineeringMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

In this dissertation a new modular design method for morphing wings is presented. First, a design method was created, applying modularity and recon gurability to a morphing wing system. With modularity being a requirement for the morphing wing system, a discretization method is developed to determine the discrete number of modules required to perform a desired morphing maneuver. Then, a specialized, modular, recon gurable variable geometry truss mechanism is proposed to facilitate morphing. The specialized modular wing truss is a recon gurable, limited mobility parallel mechanism, adapted to t within the volume of a wing. The mobility of the wing truss module is analyzed via a branch-based mobility and connectivity analysis that imposes kinematic requirements on the truss mechanism. The mobility and connectivity requirements are used to perform an enumeration analysis to isolate candidate module con gurations for morphing. Then, a parametric kinematic constraint system is developed and applied to the wing module and the kinematic performance of the module is evaluated. The kinematics are applied to a mechanical prototype of the wing module for validation purposes. Finally, the kinematics are used to evaluate the motion response of a wing skin system to lay the foundation for detailed design.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.892
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.065
GPT teacher head0.271
Teacher spread0.206 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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