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3D Simulation of Combining Flows in 90° Rectangular Closed Conduits

2006· article· en· W2062891631 on OpenAlexaff
A. S. Ramamurthy, Junying Qu, Chao Zhai

Bibliographic record

VenueJournal of Hydraulic Engineering · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Sediment Control
Canadian institutionsConcordia University
Fundersnot available
KeywordsElectrical conduitReynolds numberFlow (mathematics)MechanicsHydraulic structurePipe flowComputational fluid dynamicsGeologyEnvironmental scienceMarine engineeringComputer scienceGeotechnical engineeringEngineeringTurbulenceMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Combining flows are encountered often in environmental engineering and hydraulic engineering. Experimental data are available to assist the engineers who need the various loss coefficients associated with combining flows in closed conduits. For the combining flows in 90° rectangular conduit junctions, the Reynolds averaged Navier–Stokes equations are applied, while using the three-dimensional k-ω model. The energy loss coefficients and the mean flow pattern are obtained and validated by experimental data. The numerical modeling is less time-consuming and less expensive to obtain the various flow parameters needed for engineering design.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.004
GPT teacher head0.185
Teacher spread0.181 · 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 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

Citations21
Published2006
Admission routes1
Has abstractyes

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