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Record W3174340958 · doi:10.3390/en14133797

CFD–DEM Simulation of Sand-Retention Mechanisms in Slurry Flow

2021· article· en· W3174340958 on OpenAlex
Fatemeh Razavi, Alexandra Komrakova, Carlos F. Lange

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.

Bibliographic record

VenueEnergies · 2021
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaRGL Reservoir Management
KeywordsComputational fluid dynamicsSlurryCFD-DEMMechanicsLaminar flowDiscrete element methodParticle (ecology)Flow (mathematics)Particle sizeFluid dynamicsMaterials scienceEngineeringGeologyPhysicsComposite materialChemical engineering

Abstract

fetched live from OpenAlex

The primary motivation of this paper is to investigate the sand-retention mechanisms that occur at the opening of sand filters. Various retention mechanisms under various conditions are explored that have a particulate flow with a low concentration of sand particles (called slurry flow) such as particle shape, size, and concentration. The computational fluid dynamic (CFD)–discrete element method (DEM) model is applied to predict the retention mechanisms under steady flow conditions of the well-bore. By using coupled CFD–DEM (CFD to model the fluid flow, and DEM to model the particle flow), the physics involved in the retention mechanisms is studied. The coarse grid unresolved and the smoothed unresolved (refined grid unresolved) coupling approaches implemented in STAR-CCM+ (SIEMENS PLM) are used to transfer data between the fluid and solid phases and calculate the forces. The filter slots under investigation have different geometries: straight, keystone, wire-wrapped screen (WWS) and seamed slot and the particles are considered with different shapes and different aspect ratios and size distributions. The flow regime is laminar in all simulations conducted. The CFD–DEM model is validated from the perspectives of particle–fluid, particle–particle, and particle–wall interactions. Verification of the CFD–DEM model is conducted by mesh sensitivity analysis to investigate the coupling resolution between the CFD and DEM. By simulation of numerous slurry flow scenarios, three retention mechanisms including surface deposition, size exclusion, and sequential arching of particles are observed. However, the concentration of particles is too diluted to result in multiparticle arch formation. In the simulations, various conditions are tested to give us an insight into the parameters and conditions that could affect the occurrence of the retention mechanisms. As an example, the importance of the gravity force and interaction forces on retention mechanisms are confirmed at the microscale in comparison with others forces involved in retention mechanisms such as the drag force, lift force, cohesive force, buoyancy force, and virtual mass force.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.314

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 head0.010
GPT teacher head0.207
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