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Record W4406783807 · doi:10.1063/5.0250201

On the wake dynamics of wall-mounted helical straked cylinders

2025· article· en· W4406783807 on OpenAlexafffund
Abhinav Thakurta, Ram Balachandar

Bibliographic record

VenuePhysics of Fluids · 2025
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsWakeMechanicsDynamics (music)Classical mechanicsAcoustics

Abstract

fetched live from OpenAlex

The wake dynamics of wall-mounted circular cylinders with helical strakes are investigated using particle image velocimetry under shallow flow conditions. The helical strakes attached to the cylinder surface have a three-start configuration with a pitch equal to 10d and a height of 0.2d, where d is the cylinder diameter. The influence of strake orientation is explored by positioning the cylinder to align one of the strakes at an angle of 0° or 30° with the streamwise flow at the cylinder-bed junction. In the presence of the strakes, the wakes displayed elevated turbulence levels and reduced mean velocity, which influence the vortex formation length and other wake characteristics. The strakes induce asymmetric behavior across the depth of the flow resulting in the disruption of coherent vortex shedding. When aligned at 0° to the oncoming flow, the strakes increase the Reynolds stresses throughout the flow depth and enhance their diffusion, which can facilitate better wake mixing and momentum exchange. When the strakes are positioned at 30°, the stresses are distributed over a wider region with decreased magnitude. There is a notable increase in energy contributions near the bed for the 0° case as revealed by proper orthogonal decomposition. The spatial modes exhibit a wide range of scales across the flow depth for the straked cylinders.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.855
Threshold uncertainty score0.350

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 head0.008
GPT teacher head0.235
Teacher spread0.227 · 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

Citations9
Published2025
Admission routes2
Has abstractyes

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