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Record W2308757760 · doi:10.1115/imece2014-37393

Numerical Investigation of Vortex Shedding in a Square Cylinder Using RANS and LES Model

2014· article· en· W2308757760 on OpenAlexaff
Mohammad A. Hossain, Sarzina Hossain, Mohammad Ikthair Hossain Soiket

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCyclone Separators and Fluid Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsReynolds-averaged Navier–Stokes equationsTurbulenceMechanicsTurbulence kinetic energyLarge eddy simulationReynolds numberVortex sheddingTurbulence modelingVorticityReynolds stress equation modelPhysicsVortexFlow (mathematics)K-omega turbulence model

Abstract

fetched live from OpenAlex

This work deals with the numerical investigation of vortex shedding in a square cylinder. The simulation has been done for 2D turbulent flow. Reynolds number (Re) is considered 22000. In order to resolve the flow field, both Reynolds Average Navier-stokes (RANS) model and Large Eddy Simulation (LES) technique were used. Standard k-epsilon model is considered among different RANS model in order to resolve the mean velocity field and the LES is used to capture the flow separation in the flow field. Both average velocity and velocity fluctuation are determined and compared. Different turbulence properties such as turbulent kinetic energy, turbulent intensity, eddy viscosity and vorticity magnitude are determined and presented for different downstream location to describe the complex flow property in turbulent regime. First the mesh independence test is done by different mesh size. ANSYS Fluent is used for the simulation. A pressured based solver is used with SIMPLE solution scheme, in order to find different flow variables. The results are compared with available published data. There are significant agreements with the experimental 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 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 score0.362
Threshold uncertainty score0.268

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 head0.013
GPT teacher head0.220
Teacher spread0.207 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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