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Record W1991889478 · doi:10.1115/omae2013-11211

A CFD Simulation of Near Wall Turbulent Flow in Concentric Annulus

2013· article· en· W1991889478 on OpenAlexafffund
Mohammad Azizur Rahman, Fabio Ernesto Rodriguez Corredor, Majid Bizhani, Ergün Kuru

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputational fluid dynamicsTurbulenceAnnulus (botany)MechanicsParticle image velocimetryConcentricReynolds numberPhysicsGeometryMaterials scienceMathematics

Abstract

fetched live from OpenAlex

A CFD simulation study was conducted to analyse the near wall turbulence characteristics of water flow through concentric annulus. The continuity and momentum equations were solved by using a commercial CFD package (CFX 14) with the Shear-Stress-Transport (SST) model option. The simulation results were compared to the experimental data obtained by using high resolution Particle Image Velocimetry (PIV) analyses of water flow in a horizontal concentric annulus. A fully developed turbulent flow of water through a horizontal flow loop (ID = 9.5 cm) with concentric annular geometry (inner to outer pipe radius ratio = 0.4) was used for comparison purpose. Reynolds number ranged from 17,500 to 68,500. Annular velocity profile obtained from simulation study showed good agreement with the experimental data. Near wall velocity profile obtained from CFD simulation followed the universal wall law (u+ = y+) up to y+ = 11. CFD analyses using the SST model resulted a good number of velocity data up to y+ = 11, which is normally a very difficult task to achieve experimentally. The CFD analyses using SST model is computationally inexpensive and therefore, can be conveniently used for investigating the near wall turbulent characteristics of flow in concentric annulus.

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 score0.015
Threshold uncertainty score0.720

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 head0.005
GPT teacher head0.197
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 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
Published2013
Admission routes2
Has abstractyes

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