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Record W2946625932 · doi:10.1615/ihtc12.2090

Laminar forced convection in two-dimensional impacting tee junctions

2002· article· en· W2946625932 on OpenAlexaff
A. M. F. El-Shaboury, H.M. Soliman, Scott J. Ormiston

Bibliographic record

VenueProceeding of International Heat Transfer Conference 12 · 2002
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMechanicsLaminar flowHeat transfer coefficientHeat transferStreamlines, streaklines, and pathlinesForced convectionShear stressHeat fluxMaterials scienceAirflowReynolds numberCirculation (fluid dynamics)ConvectionThermodynamicsTurbulencePhysics

Abstract

fetched live from OpenAlex

This study presents numerical predictions of the laminar fluid-flow and heat-transfer characteristics in planar (two-dimensional) impacting tee junctions. The applicable Navier-Stokes equations and the energy equation were solved for airflow at two inlet Reynolds numbers (Re1) and a wide range of the mass split ratio (β). The results include wall shear stress distributions, streamlines showing the number, location, and size of the re-circulation zones, the pressure loss coefficient, wall heat flux distributions, isotherms, and the overall rate of heat transfer. These results indicate that two re-circulation zones always form on the inside-bend wall of the tee at all values of β and Re1. Two more re-circulation zones may form on the impacting wall of the tee depending on the values of β and Re1. It was also found that the pressure loss coefficient reaches a minimum and the overall rate of heat transfer reaches a maximum at even mass split (β = 0.5).

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 score0.003
Threshold uncertainty score0.006

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 communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.036
GPT teacher head0.252
Teacher spread0.216 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

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