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Reattached Turbulent Submerged Offset Jets on Rough Beds with Shallow Tailwater

2011· article· en· W2045407192 on OpenAlexaff
Faruk Bhuiyan, Alireza Habibzadeh, N. Rajaratnam, David Z. Zhu

Bibliographic record

VenueJournal of Hydraulic Engineering · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTailwaterTurbulenceHydraulic roughnessGeologyOffset (computer science)Hydraulic jumpMechanicsGeotechnical engineeringShear stressSurface finishJet (fluid)Flow (mathematics)PhysicsMaterials science

Abstract

fetched live from OpenAlex

This study investigates the characteristics of reattached plane turbulent offset jets in channels with rough beds and shallow tailwater depths. The flow consists of a deflecting free jet caused by the Coanda effect and the evolving wall jet past reattachment. In limited tailwater conditions, the jet flow is affected by the relative tailwater depth or the submergence parameter, which is defined with respect to the maximum B-jump at a negative step. The results show that for an offset height larger than the jet thickness, the forward-flow momentum, local maximum velocity, and wall shear stress decrease faster in the longitudinal direction in the offset jet than in plane turbulent wall jets. The influence of roughness on reattached offset jets appears to be less than that in submerged wall jets on a similar rough bed. The presented results and the comparative analysis with respect to wall jets in hydraulic jumps are significant for controlling and implementing similar flows on rough beds with variable downstream water levels.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.013
GPT teacher head0.188
Teacher spread0.175 · 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 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

Citations35
Published2011
Admission routes1
Has abstractyes

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