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Record W3196930945 · doi:10.2514/1.j060505

Dynamics of Turbulent and Nonturbulent Interfaces in Cylinder and Airfoil near Wakes

2021· article· en· W3196930945 on OpenAlexafffund
Huiying Zhang, Xiaohua Wu

Bibliographic record

VenueAIAA Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsRoyal Military College of CanadaQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsWakeVortex sheddingTurbulencePhysicsTrailing edgeAirfoilVorticityVortexEnstrophyGeometryMechanicsClassical mechanicsMathematicsReynolds number

Abstract

fetched live from OpenAlex

Velocity gradient tensor invariants are used to extract flow physics near the turbulent and nonturbulent interfaces (TNTIs) of cylinder and airfoil wakes with vortex shedding in conjunction with spatially developing direct numerical simulation. Conditional sampling is performed on fuzzy-cluster-method-resolved TNTIs using a novel subzone approach in which each instantaneous TNTI is subdivided into four categories: trough, bulge, leading edge, and trailing edge. Results of the conditionally sampled statistics, topology, and orientation of TNTI local structures suggest that wake TNTI properties depend more heavily on the degree of vortex shedding and relatively less on the degree of wake symmetry. The present subzone-sampled joint probability density functions of the second and the third invariants of the velocity gradient tensor are compared with existing jet and mixing layer observations, and new insights are extracted. Random relative orientation between the vorticity vector and the TNTI normal is observed in the trough subzone of the present wake TNTIs, which casts doubts on the notion of full vortex structure confinement. The turbulent flow near the trailing edge subzone of wake TNTI is found to be the most effective in enstrophy production, whereas the turbulent flow in the leading-edge portion is the least effective.

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 score0.294
Threshold uncertainty score0.502

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 head0.195
Teacher spread0.190 · 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

Citations4
Published2021
Admission routes2
Has abstractyes

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