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Record W2736993553 · doi:10.4050/f-0073-2017-12018

Passive Flow Control of Dynamic Stall via Surface-based Trapped Vortex Generators

2017· article· en· W2736993553 on OpenAlexaff
Khider Al-Jaburi, Dániel Feszty

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPlasma and Flow Control in Aerodynamics
Canadian institutionsCarleton University
Fundersnot available
KeywordsStall (fluid mechanics)Vortex generatorVortexFlow control (data)Control theory (sociology)Computer scienceMechanicsMaterials sciencePhysicsControl (management)Telecommunications

Abstract

fetched live from OpenAlex

A novel passive flow control concept - based on the local modification of an airfoil's surface - is proposed and examined via CFD for the mitigation of the negative effects of dynamic stall, i.e. for the reduction of peak negative pitching moment while not deteriorating significantly the original lift and drag characteristics. 2D CFD simulations of a NACA 0012 airfoil exposed to a freestream of Mach 0.3 and Re = 3.76×10⁶ and undergoing a 15°±10° pitch oscillation with a reduced frequency of 0.101 were conducted. The baseline airfoil simulations were carefully verified and validated, showing excellent agreement with wind tunnel data. Twenty-six different local geometry modifications were proposed and examined, all functioning as a trapped-vortex generator. The surface modifications were examined on both the upper and lower surfaces. In case of the upper surface modifications, the best geometries could reduce the peak negative pitching moment by as much as 37-63%, while sacrificing only 2-10% of peak lift and reducing drag by 14-38%. On the other hand, the lower surface modifications demonstrated the ability to increase lift by 4-16% with only minor penalty in pitching moment and drag.

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 score0.382
Threshold uncertainty score0.911

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 science0.0000.000
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 head0.005
GPT teacher head0.192
Teacher spread0.188 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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