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Record W1881495385 · doi:10.1115/1.4031258

Turbulent Flows Over Forward Facing Steps With Surface Roughness

2015· article· en· W1881495385 on OpenAlex
Weijie Shao, Martin Agelin‐Chaab

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

VenueJournal of Fluids Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFreestreamReynolds numberParticle image velocimetryTurbulenceSurface roughnessMechanicsSurface finishProper orthogonal decompositionSurface (topology)GeometryMaterials scienceMathematicsOpticsPhysicsComposite material

Abstract

fetched live from OpenAlex

This paper reports an investigation of the effects of surface conditions of forward-facing steps (FFS) on turbulent flows. Three surface conditions including one smooth step and two rough step surfaces created using sandpapers were studied. A particle image velocimetry (PIV) technique was used to conduct velocity measurements at several locations downstream, and the statistics up to 60 step heights are reported. The step height was maintained at 6 mm, and three Reynolds numbers of Reh = 1600, 3200, and 4800, where Reh is based on the step height and freestream mean velocity, were studied. The results indicate that the reattachment length of a FFS increases with Reynolds number but decreases with increasing surface roughness. The proper orthogonal decomposition (POD) results showed the step roughness affects even the large-scale structures.

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 score0.095
Threshold uncertainty score1.000

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 head0.009
GPT teacher head0.196
Teacher spread0.188 · 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