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Record W2012933556 · doi:10.1115/fedsm2002-31069

Experimental Investigation of the Effect of Upstream Conditions on Smooth and Rough Zero Pressure Gradient Turbulent Boundary Layer Flows at Very High Reynolds Numbers

2002· article· en· W2012933556 on OpenAlexaff
Luciano Castillo, Junghwa Seo, T. Gunnar Johansson, Horia Hangan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsWestern University
Fundersnot available
KeywordsReynolds numberBoundary layerMechanicsTurbulenceWind tunnelReynolds stressPressure gradientPhysicsAdverse pressure gradientBoundary layer thicknessMathematics

Abstract

fetched live from OpenAlex

A 2D turbulent boundary layer experiment in a zero pressure gradient (ZPG) has been carried out using two cross hot-wire probes. The mean velocity and all non-zero Reynolds stresses were measured in a number of positions, 14–28 m from the inlet of the wind tunnel over a rough and a smooth surface. Wind tunnel speeds of 10 m/s and 20 m/s were set up in order to test the effect of the upstream conditions on the downstream flow. The long test section allowed us to investigate the mean velocity and Reynolds stresses dependence on the local Reynolds number and the initial conditions at very high Reynolds number (i.e. Rθ ∼ 120,000). Furthermore, it will be shown that the mean velocity deficit profiles and some of the Reynolds stresses collapse when the upstream conditions are kept fixed for smooth and rough surface.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.006
GPT teacher head0.192
Teacher spread0.186 · 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 designObservational
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

Citations2
Published2002
Admission routes1
Has abstractyes

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