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Record W2884385484 · doi:10.1504/ijogct.2018.10014794

Three-dimensional modelling of oil sand multiphase flow in at face slurry system

2018· article· en· W2884385484 on OpenAlexaff
Jozef Szymanski, Enzu Zheng, Chaoshi Hu

Bibliographic record

VenueInternational Journal of Oil Gas and Coal Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSlurryMultiphase flowPetroleum engineeringFlow (mathematics)GeologyOil sandsFace (sociological concept)Geotechnical engineeringEnvironmental scienceMaterials scienceMechanicsMathematicsGeometryEnvironmental engineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

Optimisation of both hydraulic transportation efficiency and production costs are a necessity in the extraction of Athabasca oil sand deposits. There is currently a desire to extend hydraulic transport systems to production faces in oil sand mines using a mobile at face slurry system (AFSS). AFSS consists of multiple pipelines connected with flexible joints, a system which creates slurrified minerals from the mining faces for transportation to processing plants. This paper develops mathematic models governing friction loss associated with AFSS. Modelling of slurry flow is conducted using ANSYS-FLUENT. An arrangement of flexible pipe loops imitating the AFSS was set up to test the accuracy of modelling results. For slurry with a specific gravity of 1.44, solid volume fraction of 0.27, and a velocity of 4 m/s, the simulated pressure gradient associated with an AFSS with a diameter of 0.762 m is 220 Pa/m, compared to 158 Pa/m for the existing stationary system. [Received: March 15, 2016; Accepted: July 27, 2016]

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 score0.226
Threshold uncertainty score0.414

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 head0.011
GPT teacher head0.208
Teacher spread0.197 · 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
Published2018
Admission routes1
Has abstractyes

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