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Record W4312241009 · doi:10.1115/fedsm2022-86954

Wall Roughness Effects on Turbulent Flow Past a Near-Wall Square Cylinder

2022· article· en· W4312241009 on OpenAlexaff
Heath Chalmers, Xingjun Fang, Mark F. Tachie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsFreestreamReynolds numberBoundary layerTurbulenceParticle image velocimetryMechanicsMaterials scienceCylinderVortex sheddingSurface roughnessFlow separationGeometryReynolds stressBoundary layer thicknessSurface finishWakeOpticsPhysicsMathematicsComposite material

Abstract

fetched live from OpenAlex

Abstract Separated turbulent flows induced by two-dimensional square cylinder with different gap heights from a solid wall submerged in a thick turbulent boundary layer are investigated using a time-resolved particle image velocimetry system. Two different upstream wall conditions are implemented to investigate the effects of upstream wall roughness on gap flow. The examined gap ratios between the cylinder and the wall include 0.0, 0.5, and 2.0. The incoming Reynolds number based on the cylinder height and freestream velocity is 12, 750 and the boundary layer thicknesses are 3.6 and 7.2 times the step height, respectively, for the smooth and rough upstream wall conditions. The results are presented in terms of mean flow, Reynolds stresses, and proper orthogonal decomposition. Notably, the wall-roughness effects are most pronounced on the vertical Reynolds stresses and act to reduce the vortex shedding frequency.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.006
GPT teacher head0.191
Teacher spread0.185 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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