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Record W4403187360 · doi:10.1063/5.0231395

Scalar mixing and entrainment in an axisymmetric jet subjected to external turbulence

2024· article· en· W4403187360 on OpenAlexafffund
Khashayar F. Kohan, Susan Gaskin

Bibliographic record

VenuePhysics of Fluids · 2024
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsPhysicsEntrainment (biomusicology)TurbulenceRotational symmetryMechanicsJet (fluid)Scalar (mathematics)Mixing (physics)Stratified flowsClassical mechanicsStratified flowGeometry

Abstract

fetched live from OpenAlex

The present study aims to understand the process of turbulent entrainment into a jet, as affected by background turbulence, using scalar statistics. Planar laser-induced fluorescence was employed to capture the orthogonal cross sections of the jet at a fixed downstream station with varying background turbulence intensities and length scales. The conditional scalar profiles revealed that the thickness of the scalar turbulent/turbulent interface is greater than that of the traditional turbulent/non-turbulent interface, and the interfacial thickness is an increasing function of the background turbulence intensity. Although nibbling remains the primary entrainment mechanism in the far field, increased occurrence of concentration “holes” within the interfacial layer in the presence of ambient turbulence suggests a more significant role of large-scale engulfment in the turbulent/turbulent entrainment process (although still below 1% of the total mass flux). Enhanced contribution of the area of detached jet patches (i.e., “islands”) to that of the main jet is hypothesized to be evidence of intense detrainment events in the background turbulence. This can potentially contribute to a reduced net entrainment into the jet, which manifests as less negative values of scalar skewness within the jet core.

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 score0.916
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.224
Teacher spread0.216 · 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

Citations9
Published2024
Admission routes2
Has abstractyes

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