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Record W2194420742 · doi:10.1115/omae2015-42258

Coupled Heat and Mass Transfer CFD Model for Methane Hydrate

2015· article· en· W2194420742 on OpenAlexaff
Eugenio Turco Neto, Mohammad Azizur Rahman, Syed Imtiaz, Thiago dos Santos Pereira, Fernanda Soares de Sousa

Bibliographic record

VenueVolume 10: Petroleum Technology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsClathrate hydrateFlow assuranceMultiphase flowHydratePetroleum engineeringPressure dropComputational fluid dynamicsNatural gasMass transferPipeline transportMethaneFluid dynamicsHeat transferMechanicsFlow (mathematics)ThermodynamicsEnvironmental scienceGeologyChemistryEngineeringWaste managementEnvironmental engineeringPhysics

Abstract

fetched live from OpenAlex

The gas hydrates problem has been growing in offshore deep water condition where due to low temperature and high pressure hydrate formation becomes more favorable. Several studies have been done to predict the influence of gas hydrate formation in natural gas flow pipeline. However, the effects of multiphase hydrodynamic properties on hydrate formation are missing in these studies. The use of CFD to simulate gas hydrate formation can overcome this gap. In this study a computational fluid dynamics (CFD) model has been developed for mass, heat and momentum transfer for better understanding natural gas hydrate formation and its migration into the pipelines using ANSYS CFX-14. The problem considered in this study is a three-dimensional multiphase-flow model based on Simon Lo (2003) study, which considered the oil-dominant flow in a pipeline with hydrate formation around water droplets dispersed into the oil phase. The results obtained in this study will be useful in designing a multiphase flow metering and a pump to overcome the pressure drop caused by hydrate formation in multiphase petroleum production.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.933

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)0.0010.000

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 head0.016
GPT teacher head0.224
Teacher spread0.208 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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Same venueVolume 10: Petroleum TechnologySame topicMethane Hydrates and Related PhenomenaFrench-language works237,207