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The study the angle of attack and lift magnitude of a wing using COMSOL software

2023· article· en· W4388743642 on OpenAlexaff
Runpeng Chen, Yongqi Zhao

Bibliographic record

VenueTheoretical and Natural Science · 2023
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAngle of attackLift coefficientWingVortex liftWing twistLift (data mining)Chord (peer-to-peer)Wingtip vorticesWing loadingLift-to-drag ratioDragMechanicsAerodynamic centerDrag coefficientAerospace engineeringPhysicsPitching momentEngineeringAerodynamicsComputer scienceTurbulenceHorseshoe vortexVortexReynolds number

Abstract

fetched live from OpenAlex

Angle of attack (A.O.A.) is defined as the angle at which the chord of an aircraft’s wing meets the relative wind. At low angles of attack, the wing could just create a small amount of lift, and it also experience a small amount of drag. As the A.O.A. increases, both lift and drag will increase. However, when the wing reaches a critical angle of attack, the lift it could produce will quickly decrease, since the separation of the air flow and the wing surface. The objective of this study is to find the relationship between the angle of attack and the lift coefficient(which is proportional to the lift it could produce) of the wing. And as a conclusion, we find that the A.O.A. increase, the lift coefficient will also increase, and if the inlet velocity, the wing’s surface area and the velocity remains constant, when the lift coefficient increase, the lift will also increase.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.002

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 head0.264
Teacher spread0.254 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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