MétaCan
Menu
Back to cohort
Record W4415586205 · doi:10.1063/5.0285279

On the effect of varying momentum coefficient on separation control for a thick symmetric airfoil

2025· article· en· W4415586205 on OpenAlexafffund
Karen Xu, Philippe Lavoie, Pierre E. Sullivan

Bibliographic record

VenuePhysics of Fluids · 2025
Typearticle
Languageen
FieldEngineering
TopicPlasma and Flow Control in Aerodynamics
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAirfoilReynolds numberMomentum (technical analysis)VortexParticle image velocimetryFlow separationMomentum transferNACA airfoilLift (data mining)

Abstract

fetched live from OpenAlex

This study examined flow separation control on a National Advisory Committee for Aeronautics 0025 airfoil through an array of microblowers, systematically analyzing the effects of momentum coefficient (Cμ) at a chord-based Reynolds number of Rec=100 000 and 10° angle of attack. Surface pressure measurements and particle image velocimetry characterized the lift recovery and flow dynamics across different forcing frequencies (F+) and locations (xj/c) over a range of momentum coefficients. Results revealed that lower reduced frequencies (F+∼1) required a lower momentum coefficient threshold for effective flow control than higher frequencies (F+∼10). Progressively increasing Cμ systematically energized Kelvin–Helmholtz waves within the separated shear layer, with wave structures critically dependent on the forcing frequency. These energized vortices enhanced momentum transfer from the freestream, facilitating reattachment of the separated shear layer to the airfoil surface. Beyond initial reattachment, further increases in momentum coefficient produced frequency-specific modifications to the attached flow characteristics.

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 score0.319
Threshold uncertainty score0.393

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 head0.006
GPT teacher head0.234
Teacher spread0.229 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venuePhysics of FluidsSame topicPlasma and Flow Control in AerodynamicsFrench-language works237,207