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Turbulence Characteristics of Classical Hydraulic Jump Using DES

2018· article· en· W2790729067 on OpenAlexaff
Vimaldoss Jesudhas, Ram Balachandar, Vesselina Roussinova, Ron M. Barron

Bibliographic record

VenueJournal of Hydraulic Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsHydraulic jumpFroude numberVolume of fluid methodTurbulenceMechanicsFree surfaceOpen-channel flowReynolds numberLarge eddy simulationJet (fluid)JumpGeologyBoundary layerFlow (mathematics)Geotechnical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper performs a three-dimensional, unsteady, detached-eddy simulation (DES) of a classical hydraulic jump with an inlet Froude number of 8.5. The volume of fluid (VOF) method with a high-resolution interface capturing (HRIC) scheme is used for free-surface tracking. The computational results are validated using available experimental results and by ensuring that details of the flow physics based on existing knowledge are properly captured. The three-dimensional nature of the flow in the developed zone of the hydraulic jump is well demonstrated, and a better understanding of the interaction between the wall-jet flow and the roller region above it is revealed. The paper also resolves the internal turbulent structure of the classical hydraulic jump, which is not completely realized in the experimental results. Quadrant decomposition of the Reynolds shear stresses reveals that inward and outward interactions dominate the flow field. This is further ascertained by the analysis of the third-order moments of the velocity field. It is also revealed that the expanding shear layer interacts with the free surface resulting in intense undulations and breaking up of the free surface.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

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 studies0.0000.001
Scholarly communication0.0000.000
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 head0.012
GPT teacher head0.222
Teacher spread0.211 · 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

Citations49
Published2018
Admission routes1
Has abstractyes

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