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Record W4220674964 · doi:10.1080/15435075.2022.2050376

Flow control of a wind-turbine airfoil with a leading-edge spherical dimple

2022· article· en· W4220674964 on OpenAlexaff
Sujit Roy, Agnimitra Biswas, Biplab Das, Bale V. Reddy

Bibliographic record

VenueInternational Journal of Green Energy · 2022
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsAirfoilTrailing edgeDimpleStall (fluid mechanics)MechanicsLeading edgeAngle of attackLift coefficientReynolds numberLift-to-drag ratioChord (peer-to-peer)NACA airfoilTurbulenceAerodynamicsPhysicsMaterials scienceComputer scienceComposite material

Abstract

fetched live from OpenAlex

The flow separation occurred at an early angle of attack (AOA) in airfoil directs the researchers to focus on the methods of flow controlling. The present study incorporated a spherical dimple on the NACA (National Advisory Committee for Aeronautics) 4415 leading edge as a passive flow control device and compared the aerodynamic performances with the plain NACA 4415 airfoil. The dimple diameter (d) was varied from 1% to 6% of the chord length (0.01C-0.06C) to generate four numbers of the modified airfoil. A chord-based Reynolds number (Re) of 2 × 105 was selected for the present study. Shear-Stress Transport (SST) k-ω turbulence model with SIMPLE (semi-implicit method for pressure linked equations) scheme was chosen to solve the present problem computationally in ANSYS FLUENT 14.0. The results showed that the modification helped in delaying stall by 6° at the expense of 0.8% maximum lift coefficient with a dimple diameter of 0.01C. The modified airfoils experienced a primary low-velocity circular zone near the trailing edge and a secondary circulation zone at the dimple edges. In contrast, only one larger circulation zone was present near the plain airfoil trailing edge. The smallest dimple (d = 0.01C) showed a maximum lift enhancement ratio and lift to drag enhancement ratio of 14.8% and 7.86%, respectively.

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)0.000
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 score0.366
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.006
GPT teacher head0.221
Teacher spread0.215 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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