Modeling the hydrodynamic forces between fluid–granular medium by coupling DEM–CFD

Al-Arkawazi, S.; Marie, Christophe; Benhabib, Karim; Coorevits, Patrice

doi:10.1016/j.cherd.2016.11.002

Cited by 27 publications

(16 citation statements)

References 14 publications

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“…Figure 5- Figure 6 is revealed the relation between the bed height (normalized bed heights) and the inlet liquid velocity. Comparison with the experimental results [35], we seek to find the appropriate model of porosity. Figure 6 reveals that the bed height increase for three models (Mod 2, Mod 3 and Mod 4) with increase the inlet liquid velocity.…”

Section: Resultsmentioning

confidence: 99%

“…This combine is provided an effective method for numerical simulations of fluid-solid fluidized beds. In continuum approach, the fluid phase is evolved in the computational fluid dynamics (CFD) system, and the solid phase is treated by the discrete approach (DEM) [32][33][34][35].…”

Section: Methodsmentioning

confidence: 99%

“…The continuum approach (Computational Fluid Dynamics (CFD)) has developed in last two decades with the increase of capacity of the computers. The CFD codes are able to solve the governing equations of fluid flow for laminar and/or turbulent flows [35]. In this study, the CFD code is code_Saturne (version 3.0) and Salome platform (version 7) for design the geometry of the case study.…”

Section: Continuum Approach Modelmentioning

confidence: 99%

“…Fourth model (Mod4) calculates a local porosity from a REV (Representative Elemental Volume) around each particle, detailed in [35]:…”

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confidence: 99%

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Comparison of porosity models for fluidized beds

Flamarz

2017

KJAR

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Abstract:The paper describes a comparison between four numerical models of porosity, for better understanding the influence of porosity on the hydrodynamics (macroscopic and microscopic) behaviors of fluidized beds. The study has been done through an approach based on combine discretecontinuum to achieve the simulations. This approach deals with the modeling of the interactions between the fluid-solid. In which the discrete approach is used for localizing the position and velocity of each individual particles based on Newton's 2 nd law of motion, using a numerical time stepping scheme. While the continuum approach is based on finite volume method, which is solved the fluid flow equations (Navier-Stokes equations). The geometry of fluidizing column was; column diameter (D=0.096 m) and column height (H=1 m). Glass grains were firstly deposited inside the column, and then subjected to the range of inlet water velocity (0-0.14 m.s -1 ). The results of simulations are point out those four models of porosity, achieved comparable results for simulating fluidized beds. The simulations results were compared and showed a good agreement and consistency with the experimental data in the literatures. In other hand, the simulations results revealed that the models of porosity, which based on the microscopic scale, are most reliable between the models of porosity. However the differences between these models must be analyzed and kept in mind in order to select the appropriate porosity model. The results revealed that the porosity is an important parameter which effect on the hydrodynamics behavior of fluidized beds during the fluidization processes.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Continuum Approach Modelmentioning

confidence: 99%

“…Fourth model (Mod4) calculates a local porosity from a REV (Representative Elemental Volume) around each particle, detailed in [35]:…”

mentioning

confidence: 99%

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Comparison of porosity models for fluidized beds

Flamarz

2017

KJAR

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“…e capability and use of the CFD-DEM combination have been reported recently in the literature for a wide range of applications, including fluidized beds [14][15][16][17][18][19], filtration processes [20][21][22][23][24], hole cleaning and sediment transport in the oil and gas industry [25][26][27][28], hydrocyclones, vortex flow, and instabilities [29][30][31], and bed-load transport [32,33]. Specifics of the CFD-DEM coupling have also been documented, including studies of different types of DEM coupling [34], numerical errors involved in the models [35], requirements for the coupling with distinct fluid mechanics numerical models [36][37][38], and the use of nonspherical particles [39].…”

Section: Introductionmentioning

confidence: 99%

Numerical Evaluation of CFD‐DEM Coupling Applied to Lost Circulation Control: Effects of Particle and Flow Inertia

Barbosa

Lai

Junqueira

2019

Mathematical Problems in Engineering

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In the present study, the transport and deposition of solid particles to mitigate the loss circulation of fluid through a fracture transversely placed to a vertical channel is numerically investigated. These solid particles (commonly known in the industry as lost circulation materials—LCMs) are injected into the flow during the drilling operation in the petroleum industry, in hopes to control the fluid loss. The numerical simulation of the process follows a two-stage process: the first characterizes the lost circulation flow and the second the particle injection. The numerical model comprises an Eulerian–Lagrangian approach, in which the dense discrete phase model (DDPM) is combined with the discrete element method (DEM). A parametric analysis is done by varying the vertical channel Reynolds number, the particle-to-fluid density ratio, and the particle diameter. Results are shown in terms of the particle’s bed geometric characteristics, focusing on the location inside the fracture where the particles deposit, and the particle bed length, height, and time spent to fill the fracture. Also monitored are the fluid loss reduction over time and the fractured channel bottom pressure (which can be related to the fracture pressure). Results indicate that using a slow/intermediate flow velocity, associated with heavy particles with small diameters, provides the best combination for the efficient mitigation of the fluid loss process.

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Experimental methods in chemical engineering: Unresolved CFD‐DEM

2020

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CFD‐DEM combines computational fluid dynamics (CFD), which solves the equation of motion of gas or liquids, with the discrete element method (DEM), a simulation technique based on a Lagrangian description of particle motion that predicts the flow of granular matter and powders. Resolved CFD‐DEM solves the fluid motion with CFD at a scale smaller than the particle diameter (d p), assuming no‐slip on the particle surface to couple the phases. The fluid solver scale is coarser than d p in unresolved CFD‐DEM and virtual mass, drag, and other solid‐fluid forces couple the phases. Resolved CFD‐DEM is more accurate, but is orders of magnitude more computationally intensive. Unresolved CFD‐DEM predicts solid distribution, pressure loss, mass flow rate, and dense and dilute phase flow patterns when the solid to fluid and fluid to solid coupling between the fluid phase and the solid phase are non‐trivial. Researchers apply CFD‐DEM to predict gas‐fluid dynamics of fluidized beds, spouted beds, hoppers, cyclones, costal erosion, and rock slides. Open source codes, commercial software, and parallel computer architectures have accelerated its adoption in pharmaceutical, agro‐industrial, and reactor design. Current research targets improving the solid‐fluid coupling strategies and multiphysics problems including heat transfer, mass transfer, and chemical reactions within or at the surface of the particles. The field has grown to over 200 indexed articles per year (Web of Science) in 2018. This article is part of a special series dedicated to experimental methods in chemical engineering that reviews the most important concepts, applications, and limitations of each technique.

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Modeling the hydrodynamic forces between fluid–granular medium by coupling DEM–CFD

Cited by 27 publications

References 14 publications

Comparison of porosity models for fluidized beds

Comparison of porosity models for fluidized beds

Numerical Evaluation of CFD‐DEM Coupling Applied to Lost Circulation Control: Effects of Particle and Flow Inertia

Experimental methods in chemical engineering: Unresolved CFD‐DEM

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