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Record W2092535433 · doi:10.1002/cjce.5450780604

Hydrodynamics in a gravity settling vessel: CFD modelling with LDA validation

2000· article· en· W2092535433 on OpenAlex
Minying Wan, B. Bara, Larry P. Hackman, Jan Czarnecki, Artin Afacan, K. Nandakumar, Jacob H. Masliyah

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2000
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsUniversity of AlbertaSyncrude (Canada)
FundersSyncrude
KeywordsComputational fluid dynamicsTurbulenceSettlingMechanicsReynolds stressFlow (mathematics)Reynolds numberSlurryLaminar flowGeologyPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract Vertical gravity settling vessels, usually referred to as primary separation vessels (PSV), are used in separating bitumen aggregates from slurry containing sand and fine clays. The hydrodynamics in the PSV influences the separation efficiency of recovered bitumen through the overall mean flow and turbulent interaction. In order to deepen our understanding of the hydrodynamic conditions in such vessels, this paper presents a combined study of the flow field using Laser Doppler Anemometry (LDA) to measure the velocity field, and computational fluid dynamics (CFD) simulations to validate the CFD model. The investigation shows that the flow geometry has a significant influence on the overall flow pattern in such vessels. It also demonstrates that the CFD simulation is a reliable tool in capturing the complex mean flow pattern observed in experiments. Use of different turbulent models such as the standard k‐ε model and Reynolds stress model has very little effect on the mean flow field.

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.

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 score0.005
Threshold uncertainty score0.412

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 head0.004
GPT teacher head0.157
Teacher spread0.153 · 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