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Spatial features of Reynolds-stress carrying structures in turbulent boundary layers with pressure gradient

2025· article· en· W4411299913 on OpenAlexafffund
Mehmet Ali Yesildag, Taygun R. Gungor, Ayse G. Gungor, Yvan Maciel

Bibliographic record

VenueInternational Journal of Heat and Fluid Flow · 2025
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversité Laval
FundersAlliance de recherche numérique du CanadaNatural Sciences and Engineering Research Council of CanadaTürkiye Bilimsel ve Teknolojik Araştırma KurumuBarcelona Supercomputing CenterPartnership for Advanced Computing in Europe AISBL
KeywordsReynolds stressMechanicsTurbulencePressure gradientReynolds numberBoundary layerMaterials scienceAdverse pressure gradientStress (linguistics)Flow separationClassical mechanicsPhysics

Abstract

fetched live from OpenAlex

We investigate the Reynolds-shear-stress carrying structures in the outer layer of non-equilibrium pressure-gradient turbulent boundary layers using four direct numerical simulation databases, two cases of non-equilibrium pressure-gradient boundary layers and two of homogeneous shear turbulence. We examine and compare the spatial organization and shapes of the Reynolds-shear-stress structures, specifically sweeps and ejections, across all cases. The analysis includes five streamwise locations in the boundary layers, varying in pressure-gradient sign, intensity, and upstream history. For the boundary layers, two types of three-dimensional velocity fields are considered: fully spatial fields and spatio-temporal fields using Taylor’s frozen turbulence hypothesis. Comparisons of the results indicate that the statistics of sweep and ejection shapes are sensitive to the choice of convection velocity in Taylor’s hypothesis. The sweep and ejection shapes are consistent across all flows when their sizes range from 1 to 10 Corrsin length scales, suggesting that mean shear plays a similar role in all cases, driving the formation of Reynolds-shear-stress carrying structures and contributing to turbulence production. Sweeps and ejections of different types form side-by-side pairs, while structures of the same type align in an upstream–downstream configuration. This behavior persists regardless of pressure gradient variations or upstream history, emphasizing the dominant influence of local mean shear.

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 score0.168
Threshold uncertainty score0.439

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 head0.003
GPT teacher head0.206
Teacher spread0.203 · 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

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