MétaCan
Menu
Back to cohort
Record W3120924173 · doi:10.2514/6.2021-0468

Effect of Various Cambered Airfoil Profiles to Wings with Leading Edge Tubercles in Transonic Flow

2021· article· en· W3120924173 on OpenAlexaff
Robert R. Colpitts, Alexi Levert-Beaulieu, Ruben E. Perez

Bibliographic record

VenueAIAA Scitech 2021 Forum · 2021
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsAirfoilTransonicCamber (aerodynamics)Leading edgeAngle of attackAerodynamicsDragWingNACA airfoilLift-to-drag ratioLift coefficientAerospace engineeringSubsonic and transonic wind tunnelMechanicsLift (data mining)Lift-induced dragTrailing edgePhysicsEngineeringComputer scienceReynolds numberTurbulence

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2021-0468.vid The application of leading edge tubercles to subsonic airfoils has been shown to improve flow behaviour at transonic speeds. In particular an optimized tubercled shape represented by a power series function has been found to reduce transonic drag at low angles of attack when applied to a NACA 0012 airfoil. Strong indications of lift penalties along with the reduction in drag were noted for the airfoil. This paper examines the performance influences of power series tubercles applied to various cambered airfoil profiles. Results from CFD simulations, and insights into the flow behaviour are presented. It is found that the tubercle shape's influence on aerodynamic performance is dependent on the airfoil thickness and camber. Application of tubercles with cambered airfoils indicate improvement in aerodynamic efficiency of up to 6% compared to the straight leading edge wing.

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: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.002
GPT teacher head0.199
Teacher spread0.196 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAIAA Scitech 2021 ForumSame topicComputational Fluid Dynamics and AerodynamicsFrench-language works237,207