MétaCan
Menu
Back to cohort
Record W2080443794 · doi:10.2495/afm080141

Characteristics of the flow over a NACA 0012 airfoil at low Reynolds numbers

2008· article· en· W2080443794 on OpenAlexafffund
R. W. Derksen, Martin Agelin‐Chaab, Mark F. Tachie

Bibliographic record

VenueWIT transactions on engineering sciences · 2008
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReynolds numberNACA airfoilAirfoilMechanicsAngle of attackLaminar flowPhysicsParticle image velocimetryLift (data mining)VorticityVortexAerodynamicsTurbulenceComputer science

Abstract

fetched live from OpenAlex

This paper presents our work of an experimental examination of the flow over a NACA 0012 Airfoil at low Reynolds numbers and large angle of attack using particle imaging velocimetry.The Reynolds numbers examined were 5,000, 30,000, and 60,000, while the angles of attack ranged from 8 to 12 degrees in 2 degree increments.This work was motivated by reports that lift and drag measurements for airfoils operating at Reynolds numbers less than roughly 40,000 could not be made due to flow unsteadiness.This is puzzling in that the flow should be laminar at these Reynolds numbers, which are an order of magnitude lower than the flat-plate transition Reynolds number of 500,000.To this end we examined a sequence of flow field measurements of the instantaneous velocity field.We observed mean streamline patterns that were very representative of those we would find for a strictly steady flow, however a random pattern of significant fluctuations in the velocity and vorticity were observed.The intensity of these fluctuations increased with Reynolds number and angle of attack.

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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.172
Teacher spread0.166 · 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

Citations9
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueWIT transactions on engineering sciencesSame topicFluid Dynamics and Turbulent FlowsFrench-language works237,207