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Record W4232582803 · doi:10.32920/ryerson.14643981.v1

Aerodynamic analysis for module discretization and consolidation of a morphing wing

2021· preprint· en· W4232582803 on OpenAlexaff
Ryan Perera

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsWingMorphingAerodynamicsDiscretizationClimbComputer scienceAerospace engineeringEngineeringMathematicsArtificial intelligenceMathematical analysis

Abstract

fetched live from OpenAlex

Module discretization and consolidation was performed on morphing wing profiles optimized for climb, cruise, and descent flight regimes. Wing profiles were created using an optimization algorithm based on their aerodynamic performance for the three flight regimes. A module discretization method was applied for the three cases and the minimum number of modules were found for each case without significantly sacrificing performance. The three wing profiles were then consolidated into a single final wing using a newly proposed method for combining closely aligned joints based on a weighting scale for each flight regime. When the final wing’s performance was compared to the original wing profiles a reduction of 5% and 2% was observed for climb and descent configurations, respectively. The cruise configuration was found have a 3% increase when compare to the original profile. The final wing was found to successfully maintain aerodynamic performance during module discretization and consolidation process.

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 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 score0.686
Threshold uncertainty score0.504

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)0.0000.000

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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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.

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

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