A study of bubbling and slugging fluidised beds using the two-fluid granular temperature model

Pain, Christopher C.; Mansoorzadeh, Shahriar; Oliveira, C.R.E. de

doi:10.1016/s0301-9322(00)00035-5

Cited by 119 publications

(64 citation statements)

References 36 publications

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“…This approach can be applied to multiphase flow processes containing large volume fractions of dispersed phase (Fan and Zhu, 1998;Ranade, 2001). Numerous studies on the kinetic theory model to predict the momentum exchange due to particle-particle collision have validated the implementation of this approach for modelling fluidized beds (Sinclair and Jackson, 1989;Ding and Gidaspow, 1990;Gidaspow, 1994;Hrenya and Sinclair, 1997;Pain et al, 2001;Gelderbloom et al, 2003;Taghipour et al, 2005). Taking into account the loss of energy due to collision of particles which is not considered in the classical kinetic theory, Jenkins and Savage (1983) proposed the coefficient of restitution that is defined on a range of 0 for fully inelastic to 1 for fully elastic collisions.…”

Section: Introductionmentioning

confidence: 99%

CFD simulation of gas–solid bubbling fluidized bed: A new method for adjusting drag law

et al. 2009

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In computational fluid dynamics modelling of gas-solid two phase flow, drag force is one of the dominant mechanisms for interphase momentum transfer. Despite the profusion of drag models, none of the available drag functions gives accurate results in their own original form. In this work the drag correlations of Syamlal and O'Brien (Syamlal and O'Brien, Int. J. Multiphase Flow. 1988; 14(4) Hill et al. (Hill et al., J. Fluid Mech. 2001; 448:243-278) are reviewed using a multi-fluid model of FLUENT V6.3.26 (FLUENT, 2007. Fluent 6.3 User's Guide, 23.5 Eulerian Model, Fluent, Inc.) software with the resulting hydrodynamics parameters being compared with experimental data. The main contribution of this work is to propose an easy to implement and efficient method for adjustment of Di Felice drag law which is more efficient compared to the one proposed by Syamlal-O'Brien. The new method adopted in this work showed a quantitative improvement compared to the adjusted drag model of Syamlal-O'Brien. Prediction of bed expansion and pressure drop showed excellent agreement with results of experiments conducted in a Plexiglas fluidized bed. A mesh size sensitivity analysis with varied interval spacing showed that mesh interval spacing with 18 times the particle diameter and using higher order discretization methods produces acceptable results.Dans la modélisation par la dynamique des fluides par ordinateur de l'écoulement diphasique gaz-solide, la force de traînée est l'un des mécanismes dominants dans le transfert de quantité de mouvement interphase. Malgré la profusion des modéles de traînée, aucune des fonctions de traînée disponibles ne donnent de résultats précis dans leur forme originale. (FLUENT, 2007. Fluent 6.3 User's Guide, 23.5 Eulerian Model, Fluent, Inc.), les paramétres hydrodynamiques résultantsétant comparés aux données expérimentales. La principale contribution de ce travail est de proposer une méthode efficace et facileá mettre en oeuvre pour l'ajustement de la loi de traînée de Di Felice qui est plus efficace comparativementá celle proposée par Syamlal-O'Brien. La nouvelle méthode adoptée dans ce travail montre une amélioration quantitative par rapport au modéle de traînée ajusté de Syamlal-O'Brien. La prédiction de l'expansion de lit et de la perte de charge montre un excellent accord avec les résultats des expériences menées dans un lit fluidisé en plexiglass. Une analyse de sensibilité de la taille des mailles avec des mailles de taille variable variés montre qu'une taille de mailleégaleá 18 fois le diamétre des particules et l'utilisation de méthodes de discrétisation d'ordre supérieur donnent des résultats acceptables.

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Section: Introductionmentioning

confidence: 99%

CFD simulation of gas–solid bubbling fluidized bed: A new method for adjusting drag law

et al. 2009

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“…A viscosidade da partículas foi calculada pela Teoria Cinética Granular, as correlações foram descritas na Tabela 1. O valor do coeficiente de restituição partícula-partícula foi igual a 0,9 como utilizado por [2,3,6,7,8].…”

Section: Cfd Modelingunclassified

Escolha do modelo de turbulência para um leito fluidizado

et al. 2015

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O leito fluidizado proporciona um excelente contato gás-sólido e elevadas taxas de transferência de calor e massa. A fim de otimizar os processos nas últimas décadas, intensificaram-se o uso de técnicas computacionais como a Fluidodinâmica Computacional, CFD, que resolve as equações de conservação através da discretização, no método dos volumes finitos. Nas simulações foram utilizadas o pacote comercial ANSYS FLUENT 15.0 e sistema de simulação contendo ar e areia. O teste de Modelo foi realizado para identificar o modelo de turbulência mais adequado para as simulações (k-ε ou SST). A geometria foi construída em 2D e foi realizado um teste de malha, para verificar se a malha influenciava nos resultados. A interação entre as fases foi determinada pela equação Syamlal-O'Brien e o valor do coeficiente de restituição partícula-partícula de 0,9. A viscosidade das partículas foi calculada pela Teoria Cinética Granular. A velocidade de entrada do ar foi de 0,38 ms -1 . Através da análise de expansão do leito e da queda de pressão optou-se por utilizar o Modelo k-ε nas simulações seguintes, porque apresentou resultados concordantes com menor esforço computacional, em comparação com o modelo k-ω. A malha três foi escolhida.Palavras-chave: Fluidização, gaseificação, CFD, hidrodinâmicaThe fluidized bed provide an excellent gas-solid contact and high rate of heat and mass transfer. In order to optimize the processes in recent decades intensified the use of computational techniques such as Computational Fluid Dynamics, CFD, that solves the equations of conservation through the discretization of the finite volume method. In the simulations of the computational system, package ANSYS FLUENT 15.0 and simulation system involving air and sand. Model test was performed to identify the most appropriate turbulence model to simulate (k-e or SST). The geometry 2D was constructed and conducted a mesh test, to see if the mesh influenced the results. The interaction between the phases was determined by the equation Syamlal-O'brien and the value of the coefficient of restitution particle-particle was equal to 0.9. The viscosity of the particles was calculated by Kinetic Theory Granular. The inlet velocity of the air it was 0.38 ms -1 . Through the analysis of charts bed expansion and pressure drop, coupled with analysis of the volume fraction and pressure profiles, it was decided to be used in the following simulations the model k-ε because it has concordant results with less computational effort, compared with the model k-ω. The mesh three was chosen.

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“…Due to these computational limitations, the Eulerian-Lagrangian model is normally limited to a relatively small numbers of particles. Therefore, the multifluid model is the preferred choice for simulating macroscopic hydrodynamics in Eulerian-Eulerian continuum modeling with the fluid and solid phases treated as interpenetrating continuum phases and it is the most commonly used approach for fluidized bed simulations (Pain et al, 2001). They have used certain averaging techniques and assumptions as required to obtain a momentum balance for the solids phase.…”

Section: Literaturementioning

confidence: 99%

Experimental and computational study of the bed dynamics of semi‐cylindrical gas–solid fluidized bed

2009

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With computational fluid dynamics (CFD) it is possible to get a detailed view of the flow behaviour of the fluidized beds. A profound and fundamental understanding of bed dynamics such as bed pressure drop, bed expansion ratio, bed fluctuation ratio, and minimum fluidization velocity of homogeneous binary mixtures has been made in a semi-cylindrical fluidized column for gas-solid systems, resulting in a predictive model for fluidized beds. In the present work attempt has been made to study the effect of different system parameters (viz., size and density of the bed materials and initial static bed height) on the bed dynamics. The correlations for the bed expansion and bed fluctuations have been developed on the basis of dimensional analysis using these system parameters. Computational study has also been carried out using a commercial CFD package Fluent (Fluent, Inc.). A multifluid Eulerian model incorporating the kinetic theory for solid particles was applied in order to simulate the gas-solid flow. CFD simulated bed pressure drop has been compared with the experimental bed pressure drops under different conditions for which the results show good agreements.La simulation par ordinateur de la dynamique des fluides (CFD) permet de décrire le comportement desécoulements dans les lits fluidisés. Uné etude fondamentale et approfondie de la dynamique de lit, tels la perte de charge de lit, le taux d'expansion de lit, le taux de fluctuation de lit et la vitesse de fluidisation minimale de mélanges binaires homogènes, aété réalisée dans une colonne fluidisée semi-cylindrique pour des systèmes solides de gaz, permettant d'obtenir un modèle prédictif pour les lits fluidisés. Dans le présent travail, on a tenté d'étudier l'effet de différents paramètres de système (à savoir, la taille et la masse volumique des matériaux de lit et la hauteur statique initiale de lit) sur la dynamique de lit. Des corrélations ontétéétablies pour l'expansion de lit et les fluctuations de lit en s'appuyant sur l'analyse dimensionnelle de ces paramètres de système. Uneétude par ordinateur aégalementété menéeà l'aide du logiciel commercial de CFD Fluent (Fluent, Inc.). Un modèle eulérien multifluide faisant appelà la théorie cinétique pour les particules solides aété utilisé afin de simuler l'écoulement gaz-solides. La perte de charge de lit simulée par la CFD aété comparéeà la perte de charge de lit expérimentale dans différentes conditions et les résultats montrent un bon accord.

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A study of bubbling and slugging fluidised beds using the two-fluid granular temperature model

Cited by 119 publications

References 36 publications

CFD simulation of gas–solid bubbling fluidized bed: A new method for adjusting drag law

CFD simulation of gas–solid bubbling fluidized bed: A new method for adjusting drag law

Escolha do modelo de turbulência para um leito fluidizado

Experimental and computational study of the bed dynamics of semi‐cylindrical gas–solid fluidized bed

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