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Record W2049983637 · doi:10.1115/fedsm2005-77168

Experimental Study of Vortex Shedding From a Solid Sphere in Turbulent Freestream

2005· article· en· W2049983637 on OpenAlexaff
Himanshu Tyagi, Rui Liu, David S.‐K. Ting, Clifton R. Johnston

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of CalgaryUniversity of Windsor
Fundersnot available
KeywordsVortex sheddingFreestreamReynolds numberTurbulenceMechanicsPhysicsVortexStarting vortexAerodynamicsDrag coefficientDragClassical mechanicsVortex ring

Abstract

fetched live from OpenAlex

The study of vortex shedding from a sphere assumes an important role because of its relevance to numerous aerodynamic and hydrodynamic applications. Parameters such as coefficient of drag and static pressure distribution are largely influenced by vortex shedding, and it is found by past studies that the freestream turbulence can interact and alter the vortex formation and shedding drastically. Most of these studies, however, were conducted in the low Reynolds number regime and the vortex shedding results had been described only qualitatively. To better understand the aerodynamics of a sphere in turbulent flow, an experimental study was initiated in a low speed wind tunnel to quantify the vortex shedding characteristics. The Reynolds number of the flow, based on the diameter of the sphere (d), was set at 3.3 × 104, 5 × 104 and 6.6 × 104 by varying the mean flow velocity. The sphere was placed at 20D (= 7.5d) downstream from a perforated plate, where D = 37.5 mm is the size of the holes in the perforated plate, uniquely designed for generating near-isotropic turbulence. Hot-wire measurements were taken at 10D (= 3.75d), 20D (= 7.5d) and 30D (= 11.25d) downstream of the sphere in absence and presence of the perforated plate. The vortex shedding frequency was deduced from the instantaneous flow velocity data.

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 categoriesInsufficient payload (model declined to judge)
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 score0.039
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)0.0010.000

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 head0.240
Teacher spread0.233 · 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.

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

Citations3
Published2005
Admission routes1
Has abstractyes

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