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Record W2094596463 · doi:10.1002/srin.201100339

Non‐Circular Hydraulic Jump on a Moving Surface due to an Impinging Circular Free Surface Jet of Water

2012· article· en· W2094596463 on OpenAlexaff
Mohammad Mohsen Seraj, Mohamed S. Gadala

Bibliographic record

Venuesteel research international · 2012
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsReynolds numberJet (fluid)MechanicsTurbulenceFree surfaceHydraulic jumpRADIUSMaterials scienceWettingFlow (mathematics)PhysicsComposite materialComputer science

Abstract

fetched live from OpenAlex

Abstract Liquid jet impingement has many industrial cooling applications such as metal manufacturing and steel cooling on run‐out tables (ROT). The development of the wetting front around the impingement point of a jet is central in jet impingement cooling. In this paper, the effects of moving target surface and jet Reynolds number on wetted zone and on the formation and location of hydraulic jump (HJ) are explored through a series of industrial‐scale experiments of an impinging circular free surface long water jet with high Reynolds number of 11 000–50 000 and industrial jet parameters. The moving test surface impacts the radial evolution of circular wetted zone in all directions and alter the circular HJ at the wetting front into a non‐circular contour that depends on the jet Re number. The limited relations in the literature do not represent these measured shapes and do not appropriately predict radii of HJ in industrial scale. A new correlation for radius of non‐circular HJ has been derived in this study that compared more accurately to the experimental data. Numerical simulations of radial impingement flow on moving surface were performed using a variant of k – ε turbulent model and results are compared to the experimental data. The computational results for the wetting front were found to be close to the experimental data indicating the appropriate performance of the turbulent model.

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)0.002
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 score0.026
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.322
Teacher spread0.273 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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