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Record W2995343696 · doi:10.1080/00221686.2019.1671522

Offset height effect on turbulent characteristics of twin surface jets

2019· article· en· W2995343696 on OpenAlexaff
Mohammad S. Rahman, Mark F. Tachie

Bibliographic record

VenueJournal of Hydraulic Research · 2019
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTurbulenceVorticityMechanicsOffset (computer science)Reynolds numberVortexParticle image velocimetryPhysicsEntrainment (biomusicology)GeometryOpticsMathematicsAcoustics

Abstract

fetched live from OpenAlex

The characteristics of square twin surface jets at various offset heights from the free surface were studied using a particle image velocimetry technique. The offset heights were varied from 1 to 4 nozzle widths at a fixed Reynolds number of 3890. The entrainment and mixing characteristics were examined using the potential core length, merging point, combined point, maximum velocity decay, half-velocity width and were found be nearly independent of offset height in near field. The jet–surface interaction was examined by surface velocity, vorticity thickness and surface turbulence intensities. The growth rate of the vorticity thickness reduced in the interaction region; and was more severe for shorter offset height. Turbulent structures were examined using weighted joint probability density function and two-point cross-correlations between swirling strength and velocity fluctuations. Weaker turbulent events were observed for deeper jet close to a free surface at the streamwise location beyond the attachment point.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.281
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2019
Admission routes1
Has abstractyes

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