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Record W2313189754 · doi:10.1061/40647(259)33

Numerical Simulation of Granular Particles Moving in Fluid Flow

2002· article· en· W2313189754 on OpenAlexaff
Siwa Tipthavonnukul, Dave Chan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFluid simulationMechanicsComputer scienceFluid dynamicsFlow (mathematics)Physics

Abstract

fetched live from OpenAlex

A coupled method between discontinuum and continuum approaches has been developed to simulate granular particles moving in a flowing fluid. The movement of granular particles is modeled using the discrete element method, while the finite volume scheme is used to simulate the fluid flow. The existence of granules in the flow region is assumed to cause a reduction in the amount of fluid flow in that region. The flowing fluid is expected to induce drag forces on the granules which are computed based on the relative velocities between the fluid and the particles. Buoyant forces on a particle are equal to the weight of the fluid displaced by the particle. All forces acting on each individual particle are summed in determining the particle movement. An example is given to illustrate the possibility of the proposed method in simulating granular particles moving in a flowing fluid. The problem of modeling wet granular flow requires the solution of two major physical problems; granular material as discontinua and fluid as continua. The discrete element method or DEM, proposed by Cundall and Strack, conceives granular materials as the assemblage of distinct rigid particles. Particles can interact with each other or with a solid boundary only at contact points. By using the contact force-displacement law, the forces at the contact are related to the magnitude of the overlap between particles or between particle and a solid boundary. The displacements and velocities of each individual particle are calculated from the summation of all forces acting on the particle using Newton's Second Law of Motion. The movement histories of individual particles at each time-step are traced based on the force-displacement law and the Newton's Law. Various particle shapes can be used such as polygonal, elliptical, circular shape, etc. The circular shape is selected in this paper due to its simplicity. For the fluid computational part, fluid flow is modeled using a finite volume scheme. The flow field is divided into a finite number of subregions, called cell. The continuity and Navier-Stokes equations are both applied at each cell in the flow field. To couple the movement of particulate material into fluid flow, fluid/particle interactions must be taken into account. Cundall classified five types of fluid/particle interactions depending upon the situations. Babic' and Shen as well as Sun and Vinogradov considered drag forces as the interaction of fluid flow to solid particles. Chan simulated the wet granular flow by considering buoyant and drag forces as the interaction of fluid flow to particles; on the other hand, the interaction of particle to the fluid flow is represented by using the different permeability to control the flow volume. In this paper, drag and buoyant forces are included in the fluid/particle interaction. For interaction between granular particle and the flowing fluid, the existence of the solid particles will cause resistance to the flowing fluid, In essence, the discrete element method is used to track the particles movement and the location of the particles is taken into account in the continuum calculation of fluid flow by introducing local impedance. The continuum method obtains the position of granular particles, computes the flow velocity and pressure, and imposes a drag force on each particle for the DEM calculation.

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)0.001
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.209
Teacher spread0.194 · 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

Citations3
Published2002
Admission routes1
Has abstractyes

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