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Record W1999087037 · doi:10.1080/14685248.2015.1008008

Interaction of inner and outer layers in plane and radial wall jets

2015· article· en· W1999087037 on OpenAlexafffund
Rayhaneh Banyassady, Ugo Piomelli

Bibliographic record

VenueJournal of Turbulence · 2015
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReynolds numberMechanicsTurbulencePressure gradientPhysicsBoundary layerPlane (geometry)Jet (fluid)Adverse pressure gradientLarge eddy simulationClassical mechanicsGeometryMathematics

Abstract

fetched live from OpenAlex

Large eddy simulations of turbulent radial and plane wall jets were performed at different Reynolds numbers using the Lagrangian dynamic eddy viscosity subgrid-scale model. The results were validated with experimental data available in the literature. Compared to the plane ones, the radial wall jets have an extra direction for expansion, which causes faster decay rates. Thus, the resulting pressure gradient distributions are different. However, the comparison of the results with the turbulent boundary layers under adverse and favourable pressure gradients reveals that these pressure gradients are not strong enough to cause any fundamental physical difference between plane and radial wall jets. In both cases, the local Reynolds number is an important determining factor in characterisation of the flow. The joint probability density function analysis shows that the local Reynolds number determines the level of intrusion of the outer layer into the inner layer: the lower the local Reynolds number, the stronger is the interaction of the inner and outer layers. These results can be used to clarify the scatter of the reported log-law constants in the literature.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.020
GPT teacher head0.233
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), 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

Citations43
Published2015
Admission routes2
Has abstractyes

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