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Record W2810616250 · doi:10.2495/cmem-v7-n1-33-44

CFD and experimental approach on three phase gas-liquid-solid Newtonian fluid flow in horizontal pipes

2018· article· en· W2810616250 on OpenAlexaff
Rasel A. Sultan, Serag Alfarek, Mohammad Azizur Rahman, Sohrab Zendehboudi

Bibliographic record

VenueInternational Journal of Computational Methods and Experimental Measurements · 2018
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsComputational fluid dynamicsMechanicsNon-Newtonian fluidFlow (mathematics)Fluid dynamicsNewtonian fluidTwo-phase flowMaterials sciencePhysics

Abstract

fetched live from OpenAlex

This study analyses three dimensional fluid flow through horizontal pipelines with three-phase gasliquid-solid Newtonian fluids by Computational Fluid Dynamics (CFD) simulation. Validating the simulation with experimental data, the study aims to develop a versatile acceptable simulation model that can be used further for different applied cases. An experimental setup is developed in our laboratory to determine slug flow (air-water) through a horizontal pipeline. Air as gas, water as liquid and silica as solid particle is used in this work. ANSYS Fluent version 16.2 is employed to perform the simulation. The Eulerian multiphase model with the Reynolds Stress Model (RSM) turbulence closure is adopted to analyse multiphase fluid flow. Parameters are selected from experimental works to validate the simulation. After a good agreement with experimental data, sensitivity analysis is conducted to observe the three phase fluid flow characteristics through horizontal flow. Pressure gradient (pressure drop per unit length) and in situ concentration profile are used as primary parameters. This article provides a clear relationship between the different parameters of three-phase fluid flow through a horizontal pipeline.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.047
GPT teacher head0.377
Teacher spread0.330 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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