MétaCan
Menu
Back to cohort
Record W4303062838 · doi:10.1615/tsfp10.530

STATISTICS OF TURBULENT AND LAMINARIZING FLOW IN A CIRCULAR PIPE WITH A GRADUAL EXPANSION

2017· article· en· W4303062838 on OpenAlexaff
N. Moallemi, Joshua Brinkerhoff

Bibliographic record

VenueProceeding of Tenth International Symposium on Turbulence and Shear Flow Phenomena · 2017
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of British Columbia, Okanagan CampusOkanagan University CollegeUniversity of British Columbia
Fundersnot available
KeywordsTurbulenceMechanicsReynolds numberPipe flowReynolds stressHele-Shaw flowFlow (mathematics)Shear stressPhysicsOpen-channel flowVorticityFlow separationRotational symmetryVortexClassical mechanics

Abstract

fetched live from OpenAlex

Direct numerical simulations of turbulent flow in an axisymmetric pipe with a gradual expansion have been performed for a Reynolds number of 5300 based on the inlet pipe diameter and bulk velocity in order to investigate the effect of a gradual expansion on the flow structure, with special focus on the recirculation and laminarization of the flow downstream of the expansion. An annular ribbed turbolator is used in conjunction with a periodic flow mapping approach to produce fully-developed turbulent flow conditions upstream of the gradual expansion. A turbulent free shear layer initiated at the start of the sudden expansion develops from the flow separation and then reattaches to the pipe wall downstream of the expansion. Following reattachment, the turbulent flow laminarizes as the strength and size of vortical structures gradually diminish. The laminarization process is described in terms of the evolution of the Reynolds stress tensor in the turbulent and laminarizing regions, morphometrically in terms of the evolution of coherent vortical structures, and mechanistically through analysis of the budgets of the vorticity transport equation.

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 score0.513
Threshold uncertainty score0.704

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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueProceeding of Tenth International Symposium on Turbulence and Shear Flow PhenomenaSame topicFluid Dynamics and Turbulent FlowsFrench-language works237,207