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Record W2902314315 · doi:10.2514/1.j057420

Three-Dimensional Wake of Nonconventional Vortex Generators

2018· article· en· W2902314315 on OpenAlexaff
Sen Wang, Sina Ghaemi

Bibliographic record

VenueAIAA Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWakeFreestreamParticle image velocimetryVortexMechanicsPhysicsTurbulenceTurbulence kinetic energyReynolds numberDragBoundary layerDrag coefficientWake turbulenceLaminar flowVortex generatorVortex shedding

Abstract

fetched live from OpenAlex

The wakes of wishbone, doublet, and ramp-type vortex generators (VGs) were investigated. The VGs were placed in the thin laminar boundary layer of a flat plate at a Reynolds number of 930 based on the freestream velocity and VG height. The turbulence statistics of the wake were measured with high spatial resolution using planar particle image velocimetry (PIV) and stereoscopic PIV. Three-dimensional time-resolved tomographic PIV was also carried out to visualize the evolution of vortices. The fastest recovery of the wake deficit was observed for the wishbone VG. The peak of turbulence production in the wake of the wishbone and doublet VGs had a similar magnitude and was 1.5 times stronger than that of the ramp VG. The hairpin vortices generated by the ramp VG formed the largest percentage of the wake turbulent kinetic energy, and their size is about half of the hairpins produced by the wishbone and doublet VGs. The wishbone and ramp VGs had the best overall performance. The wishbone VG generated the strongest mixing in the wake region, whereas the ramp VG had the smallest drag coefficient. The doublet VG had the weakest overall performance due to low mixing and the largest 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 categoriesInsufficient payload (model declined to judge)
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 score0.628
Threshold uncertainty score1.000

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)0.0010.000

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 head0.202
Teacher spread0.194 · 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.

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
Published2018
Admission routes1
Has abstractyes

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