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Record W2091807105 · doi:10.2514/1.37081

Numerical Investigation of Three-Dimensional Laminar Wall Jet of Newtonian and Non-Newtonian Fluids

2008· article· en· W2091807105 on OpenAlexaff
K. K. Adane, Mark F. Tachie

Bibliographic record

VenueAIAA Journal · 2008
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsReynolds numberLaminar flowNewtonian fluidMechanicsJet (fluid)PhysicsGeneralized Newtonian fluidHerschel–Bulkley fluidCompressibilityNon-Newtonian fluidClassical mechanicsViscosityTurbulenceThermodynamicsShear rate

Abstract

fetched live from OpenAlex

Three-dimensional laminar wall jets of a Newtonian fluid and two shear-thinning non-Newtonian fluids were numerically investigated. The complete nonlinear incompressible Navier-Stokes equation was solved using a colocated finite volume based in-house computational fluid dynamics code. For each fluid, the computation was performed at three Reynolds numbers. The results showed that the streamwise velocity profiles for the Newtonian fluid became self-similar but the more shear-thinning fluid never achieved a self-similar condition. Significant differences were observed among the profiles for the various fluids in the inner region. Although the transverse and spanwise components of the velocity decreased substantially with increasing Reynolds number, the values for the non-Newtonian fluids were generally an order of magnitude larger than the corresponding values for the Newtonian fluid. Depending on the specific fluid and Reynolds number, the apparent viscosities were up to 4 orders of magnitude higher than the dynamic viscosity of water. Consequently, the spread of the jet in both the transverse and spanwise directions, decay of the maximum streamwise velocity, and the skin friction coefficient depend strongly on both Reynolds number and nature of the fluid. The results also show that the jet half-width in the transverse direction is significantly higher than in the spanwise direction.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.485

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 head0.009
GPT teacher head0.187
Teacher spread0.178 · 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

Citations8
Published2008
Admission routes1
Has abstractyes

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