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Record W2163726508 · doi:10.2514/6.2009-3565

Low Reynolds Number Turbulent Flow Over Smooth and Transitionally Rough Surfaces

2009· article· en· W2163726508 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venue39th AIAA Fluid Dynamics Conference · 2009
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Manitoba
FundersManitoba HydroUniversity of Manitoba
KeywordsTurbulenceReynolds numberFlow (mathematics)MechanicsPhysicsMathematics

Abstract

fetched live from OpenAlex

This paper reports on experiments conducted to study the structure of turbulent flows over a hydraulically smooth surface and a transitionally rough surface produced using a wire mesh. A high resolution particle image velocimetry (PIV) technique was employed to conduct the velocity measurements. The smooth wall measurements were performed at two different Reynolds numbers while the wire mesh data were obtained for a single Reynolds number. From the PIV data, profiles of the mean velocity, turbulent intensities and Reynolds shear stress as well as two-point velocity correlations were obtained. The results show that the mean velocity and one-point turbulence statistics are similar outside the roughness sublayer. It is also observed that changes in Reynolds number and surface condition do not have significant effects on the average inclination of the hair-pin vortex and the streamwise and transverse extents of the two-point velocity correlations.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.229
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
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 head0.006
GPT teacher head0.199
Teacher spread0.193 · 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