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Record W3135534527 · doi:10.1063/5.0033869

Characteristics of self-oscillating twin jets

2021· article· en· W3135534527 on OpenAlexafffund
M. Mosavati, Ram Balachandar, R. M. Barron

Bibliographic record

VenuePhysics of Fluids · 2021
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsTurbulenceNozzleMechanicsReynolds stressReynolds numberTurbulence kinetic energyVortexJet (fluid)Shear stressClassical mechanicsThermodynamics

Abstract

fetched live from OpenAlex

This study is focused on the behavior of self-oscillating twin jets emanating from round and square cross section nozzles into a narrow cavity. Computational fluid dynamics simulations are carried out in a confined rectangular cavity using the Reynolds stress turbulence model. Flow field characteristics are evaluated at nozzle spacing-to-diameter ratios (S/d) of 2, 3, 4, 5, at a jet Reynolds number of 27 000 based on nozzle exit velocity and diameter (d). Effects of nozzle spacing on the frequency of oscillation, mean velocity, vortex structure, and turbulence features are examined. For S/d up to four, the two jets merge downstream and oscillate as an equivalent single jet. At larger spacing, the two jets do not merge but oscillate separately between the sidewalls and cavity centerline. Comparison of round and square twin jets demonstrates that the nozzle shape does not significantly affect the jet decay. The turbulence intensity of twin jets shows higher values at the center of the cavity for S/d < 5 and around the centerline of each jet for S/d = 5. With increasing nozzle spacing, the Reynolds shear stress demonstrates that mixing increases in the inner shear layer region and the Reynolds shear stress values for S/d < 5 are lower than for S/d = 5. Twin oscillating jets produce higher spread and turbulence intensity over a wider area which may be beneficial for cooling of hot devices in industrial applications.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
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 head0.013
GPT teacher head0.218
Teacher spread0.205 · 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 designBench or experimental
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

Citations14
Published2021
Admission routes2
Has abstractyes

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