MétaCan
Menu
Back to cohort
Record W3204170096 · doi:10.1115/fedsm2021-65448

Interactions Between the Shear Layer Emanating From Rectangular Cylinders and the Near Wake Region

2021· article· en· W3204170096 on OpenAlexaff
Sedem Kumahor, Samuel Addai, Mark F. Tachie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsWakeVortex sheddingParticle image velocimetryMechanicsReynolds numberVortexTurbulence kinetic energyTurbulenceReynolds stressMean flowPhysicsCylinderKármán vortex streetGeometryMathematics

Abstract

fetched live from OpenAlex

Abstract The interactions between the separated shear layer and the near wake region of rectangular cylinders of varying streamwise extents in a uniform flow are investigated using time resolved particle image velocimetry. The streamwise aspect ratios (AR) tested were 1 and 5, and the Reynolds number based on the oncoming flow velocity and cylinder height is 16200. The effects of varying AR on the mean flow, turbulent kinetic energy and Reynolds shear stresses are studied. Furthermore, the unsteady characteristics of the separation bubbles are examined in terms of frequency spectra analysis. The mean flow topology shows flow separation at the leading edge is not affected by the streamwise aspect ratios. However, the primary, secondary and wake vortexes show significant differences. Mean flow reattaches over the cylinder at 4.30 cylinder heights in the AR5 case while there is no mean reattachment in the AR1 case. The magnitudes of turbulent kinetic energy and Reynolds shear stress in the wake region are an order of magnitude higher in AR1 compared to AR5. Depending on the streamwise location, the vortex shedding motions in the near wake region reflect the dominant and second harmonic of the shear layer shedding frequency measured near the leading edge.

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 score0.440
Threshold uncertainty score0.177

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 head0.015
GPT teacher head0.225
Teacher spread0.210 · 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

Explore more

Same topicFluid Dynamics and Vibration AnalysisFrench-language works237,207