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The effect of subgrid-scale model on prediction of flow around a surface-mounted finite square cylinder

2011· article· en· W2019874361 on OpenAlexafffund
Maryam Einian, Donald J. Bergstrom, David S. Sumner

Bibliographic record

VenueJournal of Physics Conference Series · 2011
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTurbulenceCylinderMechanicsLarge eddy simulationReynolds numberReynolds stressFlow (mathematics)Boundary layerScale (ratio)Mean flowScale modelMathematicsGeometryPhysicsEngineering

Abstract

fetched live from OpenAlex

The present study investigates the effect of the subgrid-scale (SGS) stress model on large eddy simulation of flow over a finite-length square cylinder mounted vertically on a ground plane. The study specifically considers a cylinder of aspect ratio AR = 5 immersed in a thin boundary layer for a Reynolds number of Re = 500 (based on the free stream velocity and cylinder width.) Three different subgrid-scale models are considered: a Smagorinsky model, a dynamic Smagorinsky model and a dynamic nonlinear model. The mean flow structure is similar for all SGS models on both grids, although noticeable differences are observed in the size of the recirculation zone, level of resolved-scale turbulence and vortical structures for different SGS models. The dynamic Smagorinsky model appears to be especially sensitive to the grid resolution.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.016
GPT teacher head0.208
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2011
Admission routes2
Has abstractyes

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