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Record W3203315323 · doi:10.1115/fedsm2021-65593

Influence of Wall Proximity on the Wake Dynamics Behind a Square Cylinder

2021· article· en· W3203315323 on OpenAlexaff
Samuel Addai, Afua Adobea Mante, Sedem Kumahor, Xingjun Fang, Mark F. Tachie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsWakeReynolds numberCylinderMechanicsParticle image velocimetryTurbulenceReynolds stressBoundary layerGeometryShear stressPhysicsMaterials scienceOpticsMathematics

Abstract

fetched live from OpenAlex

Abstract In the present study, the effects of wall proximity on the wake dynamics behind a square cylinder subjected to a thick upstream turbulent boundary layer were experimentally investigated using particle image velocimetry. The Reynolds number based on the free-stream velocity and the cylinder height (h) was 12750 while the ratio of the turbulent boundary layer thickness to the cylinder height was 3.6. The gap distance (G) between the bottom face of the cylinder and the wall was varied, resulting in gap ratios (G/h) of 0, 0.3, 0.5, 1.0, 2.0 and 8.0. The flow topological differences among the various gap ratios were analyzed in terms of the mean flow and Reynolds stresses. The results show that as the cylinder approaches the wall, the mean flow becomes increasingly asymmetric about the horizontal centerline of the cylinder and the size of the mean separation bubbles in the cylinder wake increases. Also, the magnitudes of the Reynolds stresses decrease with decreasing gap ratio. For G/h > 0, the distributions of the streamwise Reynolds normal stress and Reynolds shear stress are concentrated along the upper and lower separated shear layers, resulting in characteristic double peaks. The distributions of the vertical Reynolds normal stress, however, are concentrated in the wake about the horizontal centerline of the cylinder and reveal only single peaks.

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 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 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 score0.064
Threshold uncertainty score0.219

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 head0.007
GPT teacher head0.201
Teacher spread0.194 · 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 teacher head, 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

Citations1
Published2021
Admission routes1
Has abstractyes

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