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Record W2991338904 · doi:10.1115/ajkfluids2019-5463

Experimental Investigation of Nozzle Orientation Effects on Mixing Characteristics of Elliptic Triple Free Jets

2019· article· en· W2991338904 on OpenAlexaff
Ella Marie Morris, Seyed Sobhan Aleyasin, Neelakash Biswas, Mark F. Tachie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNozzleTurbulenceMechanicsPlane (geometry)PhysicsMaterials scienceGeometryOpticsMathematicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract An experimental investigation of nozzle orientation effects on turbulent characteristics of elliptic triple free jets was carried out for three nozzle configurations. The first configuration had all three nozzles oriented along the minor plane (3_Minor), the next had two nozzles oriented along the minor plane and one along the major plane (2_Minor_1_Major) and the last configuration had one nozzle oriented along the minor plane and two along the major plane (1_Minor_2_Major). The experiments were conducted using modified contoured nozzles with a sharp linear contraction for a nozzle-to-nozzle distance of 4.1, a nozzle equivalent diameter of 9 mm and a Reynolds number of 10,000. The effects of nozzle orientation on the mean velocity, turbulence intensity and Reynolds shear stress were discussed. The velocity decay, jet spread, merging point, combined point and potential core length were used to characterize the effects of nozzle orientation on the mixing performance. The results show that the 3_Minor configuration had shorter potential core length and closer merging point location which are indicative of a faster mixing in the converging region. Two-point correlation, skewness and flatness factors were used to provide insight into the effects of nozzle orientation on turbulence structure and higher order turbulence statistics.

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 score0.232
Threshold uncertainty score0.392

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 head0.006
GPT teacher head0.204
Teacher spread0.199 · 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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