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Record W2797332184 · doi:10.7939/r3xw4893h

CFD Modelling of Laminar, Open-Channel Flows of Non-Newtonian Slurries

2017· article· en· W2797332184 on OpenAlexaboutno aff
Montilla Pérez

Bibliographic record

VenueUniversity of Alberta Library · 2017
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsLaminar flowComputational fluid dynamicsSlurryMechanicsChannel (broadcasting)Environmental scienceMarine engineeringEngineeringPhysicsEnvironmental engineeringTelecommunications

Abstract

fetched live from OpenAlex

Current oil sands mining and bitumen extraction methods produce a significant amount of tailings. Recent legislation in Alberta aims to guarantee operators treat their tailings and reclaim them 10 years after the end of mine life. Since 2012, oil sand companies have spent more than $1.3 billion in developing technologies to improve environmental performance and provide more sustainable operations. Generally, tailings are dewatered, and the solids concentration increases affecting the rheological properties of the mixtures. They can exhibit non-Newtonian, viscoplastic, and in some cases time-dependent behaviour which make them challenging to model. In this study, the behaviour of these clay-water-sand mixtures is studied using a commercially available CFD (Computational Fluids Dynamics) package. To achieve this, the physics of the laminar, open-channel flow of coarse particles suspended in a non-Newtonian fluid are broken down into smaller, less complex cases, to progressively validate the predictions of the CFD package. In all cases, the simulation results were compared with available experimental data. First, the laminar, open-channel flow non-Newtonian fluids is studied. The simulation results were able to predict the depth of flow, velocity field, and wall shear stress accurately. Next, fluid-particle systems are modelled in a way some mechanisms can be studied separately: shear-induced migration was studied and the simulated particle volume fraction and velocity profiles were in agreement with the experimental data. The model is unable to predict a depletion of the particle volume fraction at the wall as the experiments did. Single-particle settling in viscoplastic studied was also modelled using two available drag correlations and the particle settling velocity results were in good agreement when an equivalent Newtonian viscosity approach was used. The modelling of laminar pipeline transport of settling slurries captured the overall behaviour of the experiments; however, the CFD solver struggled with stability when the maximum particle packing concentration was approached anywhere in the flow domain. Finally, the knowledge gathered from previous modelling cases was used to study the laminar, open-channel flow of coarse particles in non-Newtonian suspension. The model developed in this study was able to predict the settling of coarse particles when compared with experimental data. It was found that particles settle predominantly in the sheared zone where they form a stationary bed, as also indicated by the velocity profiles. In addition, a parametric study was performed to determine which flow parameters and rheological properties have a significant impact on the transport of coarse particles suspended in a non-Newtonian carrier fluid. The simulation results showed that the flow rate, mixture density, and bulk particle volume fraction are the most impactful parameters in hindering coarse particle settling. The variation of the mixture yield stress had no significant effect on coarse particle settling. An increase in particle diameter had an increasing effect on particle settling. Replacing the semi-circular channel geometry by an equivalent rectangular channel increased the size the depth the settled bed. The model presented in this study can be used to evaluate multiple conditions and for scaling purposes, or to enable the selection of a limited experimental matrix.

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 score0.086
Threshold uncertainty score0.466

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 communication0.0000.001
Open science0.0010.000
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 head0.171
Teacher spread0.160 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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