MétaCan
Menu
Back to cohort
Record W3216483373 · doi:10.32920/ryerson.14657097.v1

Mathematical Modeling, Design Validation, and Simulation of Morphing Mechanism for Winglet Applications

2021· preprint· en· W3216483373 on OpenAlexaff
Upasana Choudhuri

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMorphingKinematicsComputer scienceInverseMechanism (biology)SimulationControl engineeringControl theory (sociology)EngineeringControl (management)MathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Presented within this thesis is the preliminary design phases for the development of a morphing winglet mechanism. The mathematical models and analyses conducted within this thesis provide the means for bringing the design concept stage to the testing and validation phases. The kinematic modeling of a proposed design is developed. The inverse kinematics of the system are used to determine the required inputs to meet the range of motion. The velocity models for the system are established for both the forward and inverse cases. The inverse velocity models are used to establish a synchronous behaviour between the two serial linkages. Thus, allowing system operation as a redundantly actuated parallel mechanism. The results of implementation are evaluated for the initial and optimized designs. A proposed velocity profile is developed to facilitate control and desired operation of the system. This is then validated by the testing of the system response and error.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.048
GPT teacher head0.266
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicRobotic Mechanisms and DynamicsFrench-language works237,207