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Record W2972085321 · doi:10.1115/1.4044482

Experimental Investigation of Confined Swirling Flow and Its Interaction With a Bluff Body

2019· article· en· W2972085321 on OpenAlexaff
Zhang Tianxing, Mohamed Alshehhi, Lyes Khezzar, Yakang Xia, Nabil Kharoua

Bibliographic record

VenueJournal of Fluids Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicCyclone Separators and Fluid Dynamics
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsBluffTurbulenceMechanicsReynolds numberConical surfaceFlow (mathematics)PhysicsVortexGeometryMathematics

Abstract

fetched live from OpenAlex

Abstract Detailed laser Doppler velocimetry (LDV) measurements of profiles of mean axial and tangential velocities and their corresponding root-mean-square (RMS) components in confined turbulent swirling pipe flow with and without the presence of a conical bluff body have been conducted. The working fluid was water and the Reynolds number based on the bulk velocity inside the pipe was equal to 40,850. The main focus of the study was to document the interaction between turbulent swirling flow inside a pipe and in the presence of a conical bluff body. The flow structure was found in all cases to be composed of a forced-free vortex. The swirl decay was exponential for Reynolds numbers of 40,850 and 14,450. The effect of the presence of the bluff body on the flow is limited to a short region upstream of it; so that the effect on the flow structure and strength of swirl upstream can be considered negligible. The flow around the bluff body adopts a different structure where the mean axial and tangential velocities tend to assume uniform profiles with turbulence activity limited to the region close to the surface of the bluff body.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.005
GPT teacher head0.194
Teacher spread0.190 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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