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Unsteady effects in flows past stationary airfoils with Gurney flaps due to unsteady flow separations at low Reynolds numbers

2015· article· en· W4243240047 on OpenAlexaff
MATEESCU Dan

Bibliographic record

VenueINCAS BULLETIN · 2015
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsMcGill University
Fundersnot available
KeywordsAirfoilReynolds numberMechanicsFlow (mathematics)Unsteady flowMathematicsTurbulencePhysics

Abstract

fetched live from OpenAlex

This paper presents the analysis of the unsteady flows past stationary airfoils equipped with Gurney flaps at low Reynolds numbers, aiming to study the unsteady behavior of the aerodynamic coefficients due to the flow separations occurring at these Reynolds numbers.The Gurney flaps are simple but very efficient lift-increasing devices, which due to their mechanical simplicity are of particular interest for the small size micro-air-vehicles (MAV) flying at low speed and very low Reynolds number.The unsteady aerodynamic analysis is performed with an efficient time-accurate numerical method developed for the solution of the Navier-Stokes equations at low Reynolds numbers, which is second-order-accurate in time and space.The paper presents solutions for the unsteady aerodynamic coefficients of lift and drag and for the lift-to-drag ratio of several symmetric and cambered airfoils with Gurney flaps.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 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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