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Record W4387271923 · doi:10.11159/jffhmt.2023.013

Drag Reduction of a NACA Aerodynamic Airfoil: A Numerical Study

2023· article· en· W4387271923 on OpenAlexvenueno aff
Amine Agriss, Mohamed Agouzoul, Abdeslem Ettaouil

Bibliographic record

VenueJournal of Fluid Flow Heat and Mass Transfer · 2023
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAirfoilNACA airfoilDragDrag divergence Mach numberLift-induced dragAerodynamicsReduction (mathematics)MechanicsZero-lift drag coefficientParasitic dragAerodynamic dragMaterials sciencePhysicsLift-to-drag ratioAerospace engineeringGeologyMathematicsEngineeringReynolds numberGeometryTurbulence

Abstract

fetched live from OpenAlex

The primary goal of this research is to introduce a novel method for reducing drag of the NACA 0012 airfoil, aiming to enhance its aerodynamic performance.This involves strategically placing a specialized device in areas where flow separation occurs.The primary purpose is to decrease drag of the airfoil, ultimately leading to improved efficiency.To thoroughly explore this approach, extensive two-dimensional numerical simulations have been carried out, employing the computational fluid dynamics (CFD) capabilities of Ansys Fluent 17.0.The analysis is based on conditions of incompressible and laminar airflow, with particular focus on a Reynolds number of Re  1000 and a 5° angle of attack.The resultant outcomes highlight instances in which the device effectively reduces drag while simultaneously enhancing the liftto-drag ratio.Looking ahead, future research works include a more comprehensive investigation of this innovative drag reduction device across a wider range of angles of attack, thereby expanding its potential applications.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.217
Teacher spread0.210 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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