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Record W2735518959 · doi:10.1139/cjce-2017-0273

Three-dimensional numerical investigation of flow through screens as energy dissipators

2017· article· en· W2735518959 on OpenAlexvenueno aff
Rasoul Daneshfaraz, Sina Sadeghfam, Ali Ghahramanzadeh

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsnot available
Fundersnot available
KeywordsHydraulic jumpFroude numberSupercritical flowDissipationReynolds-averaged Navier–Stokes equationsSupercritical fluidSluiceMechanicsFlow (mathematics)BaffleHydraulic structureEnergy (signal processing)EngineeringGeotechnical engineeringMechanical engineeringTurbulenceThermodynamicsMathematicsPhysics

Abstract

fetched live from OpenAlex

Screens, perforated units to dissipate energy in hydraulic structures, are investigated numerically in this study. These units are part of a physical setup exposed to supercritical flows, normally created by sluice gates. The interaction of perforated screens and supercritical flows results in local complex three-dimensional flows, which can be analyzed by the application of RANS-based flow equations. The most important controlling parameters include supercritical Froude number between 2 and 10 and screen porosity of 40% and 50%. Numerical water surface profiles and energy dissipation are validated by the author’s experimental data. This paper derives a set of equations in terms of depth ratio of the hydraulic jump through the perforated screens and assesses the effect of baffles on energy dissipation. This study seeks a proof-of-concept for the application of the RANS-based technique for further application of the result to real hydraulic structures in due course.

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)0.001
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.010
GPT teacher head0.197
Teacher spread0.187 · 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

Citations30
Published2017
Admission routes1
Has abstractyes

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