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Record W2799729322 · doi:10.15406/fmrij.2018.02.00017

Comparison of numerical and experimental results over a naca0025 airfoil undergoing separation

2018· article· en· W2799729322 on OpenAlexafffund
M. Ziad Saghir, Pierre E. Sullivan, Amirhossein Ahadi

Bibliographic record

VenueFluid Mechanics Research International Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAirfoilSeparation (statistics)Materials scienceMechanicsMathematicsPhysicsStatistics

Abstract

fetched live from OpenAlex

This paper examines unsteady numerical simulation of a three-dimensional flow over two different symmetric NACA airfoils. The airfoils are at various angles of attack and various low Reynolds numbers ( ) 4 6 5 10 ~ 5 10 . The Spalart-Allmaras, and k models, as well as LES and E-LES approaches are used and compared to experimental results. The capability of each turbulent model to capture characteristics at low Reynolds number is discussed. The main objective in this study is to capture the behavior of boundary-layer separation with respect to experiments at critical Reynolds numbers and then to establish a useful solution methodology describing transition in the boundary layer of airfoils accurately. Fully attached flow, boundary-layer separation, and boundary-layer with reattachment conditions are studied. Although LES and E-LES provide the highest computation cost, results show the accuracy of these methods at low Reynolds numbers (<150 x10 3 ). For higher Reynolds number, turbulence models provide a fair agreement with experiments; while the processing cost of LES method is very high. Finally, the impact of airfoil thickness, Reynolds number, and angle of attack on the boundary-layer separation and consequently airfoil performance are studied in detail.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.044
GPT teacher head0.422
Teacher spread0.378 · 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
Published2018
Admission routes2
Has abstractyes

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