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Record W4388280665 · doi:10.1139/tcsme-2023-0028

Numerical investigation of the velocity and spreading characteristics of non-circular turbulent jets at different Reynolds numbers

2023· article· en· W4388280665 on OpenAlexvenueno aff
Sanjay Kumar, Muhammad Bilal, S. Sarwar, Zaheer Ahmed, Mujeeb Iqbal Soomro, Awais Junejo, Abdul Fatah Abbasi, Khanji Harijan

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsnot available
Fundersnot available
KeywordsTurbulenceReynolds numberMechanicsJet (fluid)PhysicsTurbulence kinetic energyLaminar flowAnemometerClassical mechanics

Abstract

fetched live from OpenAlex

The flow field characteristics of non-circular turbulent jets are investigated numerically. A steady numerical simulation is conducted using the k–ε turbulence model in Ansys Fluent software. A hotwire anemometer is utilized to collect velocity data along the jet centerline for velocity validation. The jet exit Reynolds numbers vary from 200 to 5000, covering both laminar and turbulent regimes. The mean flow field characteristics, such as mean velocity, turbulent intensity, velocity decay, and half-jet spread width, are examined. The simulated results depict that mean velocity profile decay reveals a universal decay pattern, and the lateral velocity distribution shows a top-hat-like velocity profile in the near field. It significantly changed into peak-shaped and parabolic-shaped at a far downstream distance. The turbulent intensity profile reveals that shear layer growth begins near the jet exits, and entrainment becomes strong with the Reynold number. The potential core region increases with Reynolds numbers. The jet spread width shows a linear increment with the jet exit Reynolds number. The presented numerical results agree well with the measured experimental results and are consistent with published data.

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 score0.187
Threshold uncertainty score0.410

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 head0.008
GPT teacher head0.187
Teacher spread0.179 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicAerodynamics and Acoustics in Jet FlowsFrench-language works237,207