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Record W2071667193 · doi:10.1115/omae2007-29099

Application of CFD Methods to the Analysis of the Flow in Air-Lift Pump

2007· article· en· W2071667193 on OpenAlexaff
Konstantin Pougatch, M. Salcudean

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputational fluid dynamicsLift (data mining)MechanicsMultiphase flowVolume of fluid methodVolumetric flow rateFlow (mathematics)Control volumePipe flowAirflowRange (aeronautics)Two-phase flowFluid dynamicsMaterials scienceComputer scienceSimulationMechanical engineeringEngineeringTurbulenceAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

In order to improve the understanding of the air-lift process a Computational Fluid Dynamics (CFD) model has been developed. A multi-fluid Eulerian approach is used in the modeling. Three independent interpenetrating phases are considered: a continuous liquid phase (water) and two discrete phases (air bubbles and solid particles). The simulations are conducted two-dimensionally. After the transient computational results have been obtained for a sufficiently long time period, averaging is done to obtain mean values of the flow rates for all the phases. The model has been verified by comparison with published experimental results for the air-lift pipes in the range of 300–450 m in height. Good agreement between experiment and computations has been obtained. The solution process proves to be sufficiently robust for a fairly wide range of particle sizes, densities, flow rates, pipe and injection depths, and inlet volume fractions. Flow analysis reveals the distribution of velocities and volume fractions for all phases inside the airlift pipe that improves our understanding of the process. The computational results highlight some interesting transient behavior of the multiphase flow in the pipe. The numerical model can be utilized during various stages of the design of the air-lift pumps to help answer fundamental questions on the process, and during their operation to select optimal process parameters and to address possible problems.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.785
Threshold uncertainty score0.117

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.272
Teacher spread0.266 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

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