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Numerical Simulation Software for Oil Sand Slurry Flow in Flexible Pipelines

2010· article· en· W2141402903 on OpenAlexaboutno aff
Samuel Frimpong, Oluropo Rufus Ayodele, Kwame Awuah-Offei, O. F. Brown

Bibliographic record

VenueJournal of Energy Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsPipeline transportSoftwareEngineeringTruckHydraulic machinerySlurryPetroleum engineeringSimulationMarine engineeringComputer scienceMechanical engineeringAutomotive engineering

Abstract

fetched live from OpenAlex

In order to optimize the hydraulic transportation system efficiency and cost in the surface extraction of Athabasca oil sand deposits in Canada, there is a desire to extend the hydraulic transport system to production faces in oil sands mines using mobile train of Flex-Rite-based flexible pipelines. Hydraulic transportation system based on flexible-pipe arrangement has been shown to be more competitive than the dump-truck transportation system. This flexible arrangement introduces a unique set of hydraulic transport problems which needs rigorous modeling, experimentation, and analysis to understand the system production capacities and efficiency. Part of the work presented here is an attempt to provide multiphase oil sand slurry simulation and modeling by developing a slurry flow simulator, a graphical user interface-based software, for Flex-Rite flexible-pipe train, a form of hydraulic transportation system. Such software provides a tool/platform for rigorous experimentation and analysis of flow and production capacities.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.007
GPT teacher head0.211
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2010
Admission routes1
Has abstractyes

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