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Record W2990469972 · doi:10.1115/ajkfluids2019-5478

The Effects of Nozzle Orientation on Mixing Characteristics of Elliptic Twin Free Jets

2019· article· en· W2990469972 on OpenAlexaff
Ella Marie Morris, Neelakash Biswas, Seyed Sobhan Aleyasin, Mark F. Tachie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNozzleTurbulenceJet (fluid)MechanicsPlane (geometry)PhysicsParticle image velocimetryReynolds numberOpticsPlane symmetryMaterials scienceGeometryThermodynamicsMathematics

Abstract

fetched live from OpenAlex

Abstract The effects of nozzle orientation on the mixing and turbulent characteristics of elliptical free twin jets were studied experimentally. The experiments were conducted using modified contoured nozzles with a sharp linear contraction. The centers of the nozzle pair had a separation ratio of 5.5. Four nozzle configurations were tested, one twin jet orientated along the minor plane (Twin_Minor), one twin jet orientated along the major plane (Twin_Major), one single jet orientated along the minor plane (Single_Minor) and one single jet orientated along the major plane (Single_Major). In each case, the Reynolds number based on the maximum jet velocity and the equivalent diameter was 10,000. A planar particle image velocimetry system was used to measure the velocity field in the jet symmetry plane. It was observed that the velocity decay rate is not sensitive to nozzle orientation. However, close to the jet exit the spread rate was highest in the minor plane. In addition, contour plots of Reynolds shear stress and turbulence intensities revealed significant differences between the minor and major plane. Velocity profiles showed little variation close to the jet exit, while further downstream the variations between the velocity profiles were more pronounced between the major and minor planes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.227

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 head0.003
GPT teacher head0.186
Teacher spread0.183 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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