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Record W3048081559 · doi:10.1021/acsomega.0c02307

Numerical Simulation of Polymer Dispersion Systems for Polymer Injection on Offshore Platforms

2020· article· en· W3048081559 on OpenAlexaff
Zizhen Wang, Tianyang Li, Fangxiang Wang, Lin Guan, Rui Zhang

Bibliographic record

VenueACS Omega · 2020
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsUniversity of Alberta
FundersChina National Offshore Oil Corporation
KeywordsMixing (physics)Venturi effectMaterials scienceJet (fluid)Dispersion (optics)PolymerMechanicsFluentTurbulenceComputational fluid dynamicsMechanical engineeringComposite materialOpticsEngineering

Abstract

fetched live from OpenAlex

Because of the limited space and high cost of offshore platforms, the dispersion and dissolution of the polymer are required to be of high efficiency, which is essential for polymer injection to enhance hydrocarbon recovery. The numerical simulation models of the water-powder mixing process by Venturi jetting and air-mixing were established. The multiphase flow fields in the water jet ejector, water-powder mixing head, and stirring tank were numerically simulated by FLUENT. Then, the distributions of velocity, volume fraction, pressure, and turbulent kinetic energy of each phase were obtained to evaluate the effects of polymer dispersion and the dissolution of the two mixing methods. According to the maximum velocity of the mixture at the Venturi jet, the optimized length of the throat is 25 mm in our models. The results of the air-mixing process show that a 120° angle of support rods has the best effect of water-powder mixing. The results of the present study show that compared with air-mixing, the combination of Venturi jet and the stirring tank can obtain a broader agitation range and more extensive effect on the flow field, which could uniformly disperse the polymer powder into water. This study has a guiding significance for the design of the onsite polymer injection process.

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 score0.067
Threshold uncertainty score0.366

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 head0.015
GPT teacher head0.225
Teacher spread0.210 · 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
Published2020
Admission routes1
Has abstractyes

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