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Record W2076867938 · doi:10.1080/14685240500499343

Comparison between EVM and RSM turbulence models in predicting flow and heat transfer in rib-roughened channels

2006· article· en· W2076867938 on OpenAlexaboutno aff
Ahmad K. Sleiti, Jayanta Kapat

Bibliographic record

VenueJournal of Turbulence · 2006
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsTurbulenceTurbulence modelingReynolds numberMechanicsK-epsilon turbulence modelBoundary layerReynolds stressReynolds stress equation modelPhysicsHeat transferViscosityReynolds-averaged Navier–Stokes equationsK-omega turbulence modelStatistical physicsThermodynamics

Abstract

fetched live from OpenAlex

A 3D analysis of two-equation eddy viscosity (EVMs) and Reynolds stress (RSM) turbulence models and their application to solve flow and heat transfer in rotating rib-roughened internal cooling channels is the main focus of this study. The flow in these channels is affected by ribs, rotation, buoyancy, bends and boundary conditions. The EVMs considered are the standard k–ϵ model of Launder and Spalding [1 Launder, B. E. and Spalding, D. B. 1972. Lectures in Mathematical Models of Turbulence, London, , England: Academic. [Google Scholar]], the renormalization group k–ϵ model of Yakhot and Orszag [2 Yakhot, V. and Orszag, S. A. 1986. Renormalization group analysis of turbulence: I. Basic theory. Journal of Scientific Computing, 1: 1–51. [CSA] [Google Scholar]], the realizable k–ϵ model of Shih et al. [3 Shih, T.-S., Liou, W. W., Shabbir, A., Yang, Z. and Zhu, J. 1995. A new k-e eddy viscosity model for high Reynolds number turbulent flows. Computers and Fluids, 24: 227–238. [CSA] [Google Scholar]], the standard k–ω model of Wilcox [4 Wilcox, D. C. 1998. Turbulence Modeling for CFD, 2nd, La Canada, California: DCW Industries, Inc.. [Google Scholar]] and the shear–stress transport (SST) k–ω model of Menter [5 Menter, F. R. 1994. Two-equation eddy-viscosity turbulence models for engineering applications. AIAA Journal, 32: 1598–1605. [CSA] [Google Scholar]]. The viscosity-affected near-wall region is resolved by enhanced near-wall treatment using combined two-layer model with enhanced wall functions. The results for both stationary and rotating channels showed the advantages of Reynolds stress model (RSM), Gibson and Launder [6 Gibson, M. M. and Launder, B. E. 1978. Ground effects on pressure fluctuations in the atmospheric boundary layer. Journal of Fluid Mechanics, 86: 491–511. [CSA] [Google Scholar]], Launder [7 Launder, B. E. 1989. Second-moment closure: present … and future?. International Journal of Heat and Fluid Flow, 10: 282–300. [CSA] [Google Scholar]] and Launder et al. [8 Launder, B. E., Reece, G. J. and Rodi, W. 1975. Progress in the development of a Reynolds-stress turbulence closure. Journal of Fluid Mechanics, 68: 537–566. [CSA] [Google Scholar]] in predicting the flow field and heat transfer compared to two-equation EVMs that need corrections to account for streamline curvature, buoyancy and rotation.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
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 head0.019
GPT teacher head0.235
Teacher spread0.216 · 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

Citations32
Published2006
Admission routes1
Has abstractyes

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