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Record W3134292291 · doi:10.1063/5.0037732

Evolution of turbulent pipe flow recovery over a square bar roughness element at a range of Reynolds numbers

2021· article· en· W3134292291 on OpenAlexaff
Shubham Goswami, Arman Hemmati

Bibliographic record

VenuePhysics of Fluids · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsReynolds numberTurbulenceMechanicsPhysicsReynolds stressReynolds equationReynolds stress equation modelShear stressReynolds-averaged Navier–Stokes equationsPipe flowGeometryTurbulence kinetic energyMathematics

Abstract

fetched live from OpenAlex

The Reynolds number effects and scaling on response and recovery of flow over square bar roughness elements are numerically examined at a range of Reynolds numbers between 5 × 103 and 1.56 × 105. The square bar roughness element has a height of 0.05D, where D is the pipe diameter. The response is examined using streamline plots and reattachment lengths. An asymptotic trend is observed in reattachment lengths with increasing the Reynolds number. The recovery is examined quantitatively by tracing the transport of Reynolds shear stress downstream of the roughness element. While the overall trend for recovery is similar for all Reynolds numbers, the collapse of stresses toward the wall appears earlier at lower Reynolds numbers. The recovery trends follow a power-law of diffusion toward the centerline. The point of initial response, that is, the point of collapse, appears independent from the effects of Reynolds numbers at Re ≥ 5.0 × 104.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.007
GPT teacher head0.203
Teacher spread0.197 · 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 designBench or experimental
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

Citations13
Published2021
Admission routes1
Has abstractyes

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