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Record W3035815299 · doi:10.1115/1.4047531

Reynolds-Averaged Simulation of the Fully Developed Turbulent Drag Reduction Flow in Concentric Annuli

2020· article· en· W3035815299 on OpenAlexaff
Xiao Xiong, Yan Zhang, Mohammad Azizur Rahman

Bibliographic record

VenueJournal of Fluids Engineering · 2020
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsMemorial University of Newfoundland
FundersScience and Engineering Research CouncilQatar National Research Fund
KeywordsTurbulenceMechanicsWeissenberg numberReynolds numberDragAnnulus (botany)Reynolds stressTurbulence kinetic energyReynolds stress equation modelPhysicsLaminar flowNewtonian fluidMaterials scienceClassical mechanicsK-omega turbulence model

Abstract

fetched live from OpenAlex

Abstract Reynolds-averaged modeling is performed for polymer-induced drag reduction (DR) fluid at the fully developed turbulent regime in a concentric annulus by using the commercial code, ansys-fluent. The numerical approach adopted in this study relies on a modified k–ε–v2¯–f model to characterize the turbulence and the finitely extensible nonlinear elastic-Peterlin (FENE-P) constitutive model to represent the rheological behavior of the polymer solution. The near-wall axial velocity, Reynolds stress, and turbulent kinetic energy (TKE) budget near both walls of the annulus (fixed radius ratio of 0.4) are compared in detail at a constant Reynolds number (Re=10,587) and various rheological parameters (Weissenberg number We in the range of 1–7 and the maximum polymer elongation L = 30 and 100). Current simulation has predicted the redistributions of turbulent statistics in the annulus, where the two turbulent boundary layers (TBLs) of the DR flow differ more compared to those of its Newtonian counterpart. The difference is also found to be highly dependent on the rheological properties of the viscoelastic fluid.

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)0.001
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 score0.296
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.217
Teacher spread0.204 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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