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Record W3197775214 · doi:10.18280/mmep.080419

The Influence of Variable Density on Turbulent Wall Jet: A Numerical Comparative Study

2021· article· en· W3197775214 on OpenAlexvenueno aff
Guenoune Rabah, Azeddine Soudani

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsnot available
Fundersnot available
KeywordsTurbulenceMechanicsFluentJet (fluid)EddyPlane (geometry)Computational fluid dynamicsVariable (mathematics)Large eddy simulationFlow (mathematics)PhysicsMathematicsMaterials scienceMeteorologyGeometryMathematical analysis

Abstract

fetched live from OpenAlex

The first objective of this numerical research is to help understand the influence of variable density on the structure of turbulence, through the study of a wall jet, and to validate our results with those of the experimental study of A. Soudani. The source of density variation is the mixture between two different non-reactive fluids, with a fixed temperature and pressure. A mass weighted averaging for different variables is applied to the calculation, using ANSYS FLUENT 15.0 commercial software. The principal experience consists of injecting tangentially and alternatively near the wall a gas (air-helium) different from the external flow, through a slot of height 3mm between two plane walls. Such a process permits to provoke an important density difference. The study reaches the conclusion that turbulence is strong, with a slight increase of velocity near the wall and an evident diminution of skin friction, in the case of light fluid injection. The second aim is to estimate the Kolmogorov and large eddies’ scales to construct LES grid to access instant variables in experience.

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 score0.208
Threshold uncertainty score0.556

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 head0.017
GPT teacher head0.209
Teacher spread0.192 · 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
Published2021
Admission routes1
Has abstractyes

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