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Record W2326447563 · doi:10.2514/6.2014-0040

Analysis of the Flow past Airfoils with Gurney Flaps at Low Reynolds Numbers

2014· article· en· W2326447563 on OpenAlexaff
Dan Mateescu, Araz Panahi, Valentin Roy

Bibliographic record

Venue52nd Aerospace Sciences Meeting · 2014
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsMcGill University
Fundersnot available
KeywordsAirfoilReynolds numberFlow (mathematics)Computer scienceMechanicsPhysicsTurbulence

Abstract

fetched live from OpenAlex

This paper presents the unsteady flow analysis of the low-Reynolds-number flows past stationary airfoils equipped with Gurney flaps, which are simple but very efficient liftincreasing devices. Due to its mechanical simplicity, the utilization of Gurney flaps is of particular interest for the small size micro-air-vehicles (MAV) flying at low speed and very low Reynolds number. The aerodynamic analysis is performed in the paper with an efficient time-accurate numerical method developed by the authors for the solution of the NavierStokes equations at low Reynolds numbers, which is second-order-accurate in time and space. The paper presents solutions for the aerodynamic coefficients of lift and drag and for the lift-to-drag ratio of several symmetric and cambered airfoils. It was found that although the airfoil is considered stationary, starting from a relatively small incidence (about 8 degrees) the flow becomes unsteady due to the unsteadiness of the flow separations occurring at low Reynolds numbers, and the aerodynamic coefficients display periodic oscillations in time. A detailed study is presented in the paper on the influence of various geometric and flow parameters, such as the Gurney flap height, Reynolds number, airfoil relative thickness and relative camber, on the aerodynamic coefficients of lift, drag and lift-to-drag ratio. The flow separation is also studied with the aid of flow visualizations illustrating the changes in the flow pattern at various moments in time.

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)0.001
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 score0.063
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.004
GPT teacher head0.184
Teacher spread0.180 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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