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Record W4211131378 · doi:10.1063/5.0079921

Circular turbulent wall jets in quiescent and coflowing surroundings

2022· article· en· W4211131378 on OpenAlexafffund
Mohammad Kazemi, Babak Khorsandi, Laurent Mydlarski

Bibliographic record

VenuePhysics of Fluids · 2022
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsMechanicsJet (fluid)TurbulenceEntrainment (biomusicology)

Abstract

fetched live from OpenAlex

The dynamics and mixing of circular turbulent wall jets released into both a quiescent background and coflowing stream have been investigated experimentally. The statistics of the velocity field (measured by way of acoustic Doppler velocimetry) for the wall jets emitted into a quiescent background agree well with those of the other studies. The experiments involving coflowing wall jets were undertaken at three different jet-to-coflow velocity ratios. The coflowing wall jets were found to decay and spread at slower rates and have lower mean lateral velocities compared to wall jets in quiescent surroundings. Moreover, the decay and spreading rates of the coflowing wall jets increased with increasing jet-to-coflow velocity ratios. The wall jets issued into a coflow also developed more slowly and reached self-similarity at farther downstream distances relative to those emitted into a quiescent background. Given the decreased decay rate, spreading rate, and mean lateral velocities of wall jets in the presence of a coflow, it was inferred that the entrainment into, and mixing of, the wall jets was reduced, presumably due to the suppresion by the coflow of the vortical structures that characterize wall jets in quiescent backgrounds. Finally, the root-mean-square velocities of the wall jets increased when a coflow was present, and were found to be nearly self-similar in the range of measurements studied herein, in contrast with coflowing jets (that are not released in the vicinity of a wall).

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 score0.273
Threshold uncertainty score0.515

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 head0.205
Teacher spread0.195 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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