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Numerical Modeling of Vertical Buoyant Jets Subjected to Lateral Confinement

2017· article· en· W2593066711 on OpenAlex
Xiaohui Yan, Abdolmajid Mohammadian

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Hydraulic Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFroude numberTurbulenceBuoyancyJet (fluid)MechanicsMeteorologyMixing (physics)Flow (mathematics)PhysicsGeology

Abstract

fetched live from OpenAlex

The near-field flow and mixing properties of vertical buoyant jets subjected to lateral confinement are studied numerically for different cases, including different confinement indexes and jet densimetric Froude numbers. The performances of different turbulence models are investigated, such as the standard k-ϵ turbulence model and buoyancy-modified k-ϵ model. The modeled results are compared to previous and present experimental observations. The present paper confirms that the universally accepted model (k-ϵ turbulence model) can be satisfactorily accurate, eliminating the need for an advanced modeling approach, as long as suitable modifications are performed. In contrast to previous studies, which used one single and constant value of Ptr and Pr numbers, the present study links these two numbers to the F number, which is more practical and can produce very good results. This study also makes it possible to roughly quantify the rate at which the jet concentration spread width grows and identify the location where impingement occurs, which enables engineers or researchers to perform a quick estimation of the evolution and profile of a laterally confined vertical buoyant jet.

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.

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 score0.241
Threshold uncertainty score0.638

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 head0.010
GPT teacher head0.227
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