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Record W2217442598 · doi:10.1139/cjce-2015-0063

Large eddy simulation of the turbulent multiphase flow on sandstone wastewater of hydropower stations in a vortex-type grit chamber

2015· article· en· W2217442598 on OpenAlexvenueno aff
Xuefei Ao, Xiaoling Wang, Bin Qiao, Ruijin Li, Ruirui Sun

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsnot available
Fundersnot available
KeywordsVolume of fluid methodTurbulenceVortexMechanicsLarge eddy simulationFree surfaceDetached eddy simulationFlow (mathematics)Multiphase flowGeotechnical engineeringGeologyEnvironmental scienceReynolds-averaged Navier–Stokes equationsPhysics

Abstract

fetched live from OpenAlex

Vortex-type grit chambers are commonly used for the treatment of high-turbidity sandstone wastewater in hydropower stations. Current researches on vortex-type grit chambers mainly focus on the optimization of the engineering operation parameters. Although there have been many studies on sandstone wastewater simulations in stirred tanks or hydrocyclones predicted by Reynolds-averaged Navier–Stokes, there are few reports on the large eddy simulation (LES) based prediction of the multiphase flows in a vortex-type grit chamber. The rigid-lid approximation is commonly used for free surface movement. The LES can reveal more detailed pulsation features. The volume of fluid (VOF) method can describe the interfacial turbulence characteristics for free surface movement. Thus, the VOF method was used as a surface tracking technique along with LES–Lagrangian model to study the characteristics of gas–liquid–solid multiphase flows. The flow field distributions were analyzed and the micro movement regularities of particles were discussed.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

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 head0.227
Teacher spread0.213 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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