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Record W2973740273 · doi:10.2495/cmem-v7-n4-305-315

Effect of gap on the flow characteristics in the wake of a bluff body near a wall

2019· article· en· W2973740273 on OpenAlexaff
G. Nasif, R.M. Barron, R. Balachandar

Bibliographic record

VenueInternational Journal of Computational Methods and Experimental Measurements · 2019
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsBluffWakeMechanicsFlow (mathematics)GeologyMarine engineeringEngineeringMeteorologyPhysics

Abstract

fetched live from OpenAlex

a numerical investigation is carried out to evaluate the influence of the gap between the bluff body and the bed on the wake characteristics generated in shallow flows. a sharp-edge bluff body with a fixed gap from the bed is employed in the study, and the results are compared with the no gap case. a sharp-edged bluff body was chosen to minimize the effect of reynolds number and ensure fixed flow separation points. The transient three-dimensional Navier-Stokes equations are numerically solved using a finite volume approach with the detached eddy simulation turbulence model. The flow field in this study involves two different fluids, i.e. water and the air above it. The volume of fluid method is used for tracking the free surface separating the water and air. The fluid structures that are generated in the wake are identified using the 2 -criterion. The results reveal that the gap flow will develop a new structure near the bed, which enhances the upwash flow immediately after the submerged jet is about to turn upwards due to the weak hydraulic jump. This structure plays an important role in recovering the free surface to its original shape at a shorter downstream distance from the bluff body than when there is no gap.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.247

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 science0.0000.000
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 head0.032
GPT teacher head0.375
Teacher spread0.343 · 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 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

Citations5
Published2019
Admission routes1
Has abstractyes

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