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Record W4392768580 · doi:10.1063/5.0196818

Effect of turbulent coflows on the dynamics of turbulent twin jets

2024· article· en· W4392768580 on OpenAlexaff
Farzin Homayounfar, Babak Khorsandi, Susan Gaskin

Bibliographic record

VenuePhysics of Fluids · 2024
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhysicsTurbulenceMechanicsDynamics (music)Computational fluid dynamicsClassical mechanicsStatistical physics

Abstract

fetched live from OpenAlex

The impact of turbulent coflows on the dynamics of turbulent twin round jets is investigated experimentally. Parallel twin jets, at three jet spacing values and two Reynolds number/jet-to-coflow velocity ratios, were released into turbulent coflows with two distinct levels of turbulence intensity. Velocity measurements were made using acoustic Doppler velocimetry. An increase in the coflow turbulence intensity leads to an earlier merging and combining of the jets and also accelerates the rate of decay with downstream distance of the mean centerline excess velocity of the jets. The mean velocity on the symmetry line, for different values of jet spacing, ratios of jet exit velocity to coflow mean velocity, and coflow turbulence intensity, is self-similar when scaled by the maximum mean velocity on the symmetry line and the corresponding streamwise distance. Moreover, as the turbulence level of the coflow intensifies, the turbulence intensity along the symmetry line of the jets increases. The longitudinal integral length scale on the symmetry line of the twin jets decreases as the coflow turbulence intensity increases. The energy spectra of the coflowing twin jets show that the turbulence in the coflow transfers the energy contained by the larger scales to the smaller scales at a greater rate than that which occurs for jets in a quiescent background. However, as the jet spacing increases, less energy is transferred to the smaller scales.

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 score0.265
Threshold uncertainty score0.598

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 head0.007
GPT teacher head0.228
Teacher spread0.221 · 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 routes1
Has abstractyes

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