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Record W3187384038 · doi:10.3390/computation9080085

Response of Viscoelastic Turbulent Pipeflow Past Square Bar Roughness: The Effect on Mean Flow

2021· article· en· W3187384038 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueComputation · 2021
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of Alberta
FundersCanada First Research Excellence FundAlberta Innovates
KeywordsViscoelasticityNewtonian fluidMaterials scienceBar (unit)DragRheologyMechanicsSurface roughnessTurbulenceSurface finishDeborah numberReynolds numberNon-Newtonian fluidFlow (mathematics)Composite materialPhysicsMeteorology

Abstract

fetched live from OpenAlex

The influence of viscoelastic polymer additives on response and recovery of turbulent pipeflow over square bar roughness elements was examined using Direct Numerical Simulations at a Reynolds number of 5×103. Two different bar heights for the square bar roughness elements were examined, h/D=0.05 and 0.1. A Finitely Extensible Non-linear Elastic-Peterlin (FENE-P) rheological model was employed for modeling viscoelastic fluid features. The rheological parameters for the simulation corresponded to a high concentration polymer of 160 ppm. Recirculation regions formed behind the bar elements by the viscoelastic fluid were shorter than those associated with Newtonian fluid, which was attributed to mixed effects of viscous and elastic forces due to the added polymers. The recovery of the mean viscoelastic flow was faster. The pressure losses on the surface of the roughness were larger compared to the Newtonian fluid, and the overall contribution to local drag was reduced due to viscoelastic effects.

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.

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 score0.381
Threshold uncertainty score0.341

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 head0.007
GPT teacher head0.234
Teacher spread0.227 · 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