Coupled CFD-DEM Modeling E-Book

Hamid Reza Norouzi

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 849

Veröffentlichungsjahr: 2016

Beschreibung

Discusses the CFD-DEM method of modeling which combines both the Discrete Element Method and  Computational Fluid Dynamics to simulate fluid-particle interactions.

• Deals with both theoretical and practical concepts of CFD-DEM, its numerical implementation accompanied by a hands-on numerical code in FORTRAN
• Gives examples of industrial applications

Leseprobe

COUPLED CFD-DEM MODELING

FORMULATION, IMPLEMENTATION AND APPLICATION TO MULTIPHASE FLOWS

Hamid Reza NorouziReza ZarghamiRahmat Sotudeh-GharebaghNavid Mostoufi

School of Chemical Engineering, College of Engineering,University of Tehran, Tehran, Iran

This edition first published 2016© 2016 John Wiley & Sons, Ltd

First Edition published in 2016

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Library of Congress Cataloging-in-Publication data applied for

Names: Norouzi, Hamid Reza, 1985– author. | Zarghami, Reza, 1973– author. | Sotudeh-Gharebagh, Rahmat, 1964– author. | Mostoufi, Navid, author.Title: Coupled CFD-DEM modeling : formulation, implementation and application to multiphase flows / Hamid Reza Norouzi, Reza Zarghami, Rahmat Sotudeh-Gharebagh, Navid Mostoufi.Description: Chichester, UK ; Hoboken, NJ : John Wiley & Sons, 2016. | Includes bibliographical references and index.Identifiers: LCCN 2016025074 | ISBN 9781119005131 (cloth) | ISBN 9781119005292 (epub)Subjects: LCSH: Computational fluid dynamics–Mathematical models. | Discrete element method–Mathematical models.Classification: LCC TA357.5.D37 N67 2016 | DDC 620.1/064015111–dc23LC record available at https://lccn.loc.gov/2016025074

A catalogue record for this book is available from the British Library.

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To our families, for their continuous support and understanding.

About the Authors

Hamid Reza Norouzi is currently a postdoctoral fellow at the Center for Process Design and Simulation at the University of Tehran (email: [email protected]). He has taught fluid mechanics and applied mathematics for over 3 years. He was a consultant to pharmaceutical companies. His research interests include multiphase flows and computational fluid dynamics. He holds a B.Eng degree in chemical engineering from Arak University (Iran), as well as an M.Sc. and a Ph.D. in chemical engineering from the University of Tehran. He has more than 27 publications in major international journals and conferences.

Reza Zarghami is currently Associate Professor of Chemical Engineering at the University of Tehran (email: [email protected]). He has taught advanced fluid mechanics, computational fluid dynamics, mixing, and process control for over 6 years. His research interests include nonlinear dynamics, multiphase flows, and computational fluid dynamics. He holds a B.Eng degree in chemical engineering from Iran’s Shiraz University, plus an M.Sc. and a Ph.D. in chemical engineering from the University of Tehran. He has written more than 100 publications in major international journals and conferences. He was the chairman of the international conference of (MST2015).

Rahmat Sotudeh-Gharebagh is currently Full Professor of Chemical Engineering at the University of Tehran (email: [email protected]). He has taught process modeling and simulation, transport phenomena, and fluidization courses for over 17 years. His research interests include computer-aided process design and simulation, and fluidization. He holds a B.Eng degree in chemical engineering from Iran’s Sharif University of Technology, plus an M.Sc. and a Ph.D. in Fluidization from Canada’s Ecole Polytechnique de Montréal. He has been a Visiting Professor at Qatar University. Professor Sotudeh has more than 250 publications in major international journals and conferences, plus four books and three book chapters. He is the Founder and Editor-in-Chief of Chemical Product and Process Modeling (www.degruyter.com/view/j/cppm) and winner of two prestigious awards, University of Tehran’s International Award, 2015 and Allameh award from Iran National Elite Foundation, 2015.

Navid Mostoufi is currently Full Professor of Chemical Engineering at the University of Tehran (email: [email protected]). He has taught advanced mathematics and fluid mechanics courses for over 16 years. His research interests include process modeling, simulation and optimization, and fluidization. He holds B.Eng and M.Sc. degrees in chemical engineering from Iran’s University of Tehran, plus a Ph.D. in Fluidization from Canada’s Ecole Polytechnique de Montréal. He has been a Visiting Professor at METU, Turkey. Professor Mostoufi has more than 270 publications in major international journals and conferences, plus five books and four book chapters. He is the co-author of the textbook Numerical Methods for Chemical Engineers with MATLAB Applications, published by Prentice Hall PTR in 1999. He is the Founder and Editor-in-Chief of Chemical Product and Process Modeling (www.degruyter.com/view/j/cppm) and winner of University of Tehran’s International Award, 2015. He was also the University of Tehran’s distinguished researcher, 2013.

Preface

This book provides an up-to-date description of the formulation, implementation, and applications of combined (CFD-DEM) modeling. It is an integrated text that deals with theoretical and practical concepts of CFD-DEM, its numerical implementation accompanied by a numerical code and industrial applications. In the DEM part, different contact force models for spherical and non-spherical particles, as well as free-shape bodies, are discussed, along with their applicability and limitations. In the second part, couplings between solid and fluid equations for momentum, energy, and mass for particles and fluid are described and implementation of external forces on particles in multiphase flows is presented.

Over the years, many excellent books have been published dealing with various aspects of CFD. The level of sophistication of these books varies from academic to complex industrial systems and this is the main reason why we started this book with thorough treatment of the DEM. The distinctive feature of this book is its emphasis on coupled CFD-DEM (momentum, energy, and mass) as compared with books written on CFD or DEM alone. In addition, hands-on numerical codes are also delivered with the book in order to be used by readers as is, or modified as desired.

CFD-DEM has found wide range of applications in nearly all systems dealing with solids in various fields of science, engineering and technology such as chemical, food, pharmaceutical, biochemical, mechanical, energy, material, and mineral engineering. However, the prime concern of this book is to provide a more comprehensive treatment of DEM and CFD-DEM in chemical and process engineering with applications in granular and multiphase flow systems. In these systems, the DEM is commonly used for analysis of granular flow, including solid mixers, hoppers and silos, and CFD-DEM for fluid-solid flows, such as fluidized beds and conveyers, spouted beds, coal combustors, and solid incinerators.

Experimentations on multiphase flow systems are of vital importance in research and engineering. Nevertheless, they are lengthy, cost intensive, tedious, and challenging. We are unable to conduct experiments on the micro- and meso-scales in many cases and this is why most industrial processes fail at the early stages of development, design and operation. With the constant evolution of efficient computational tools, we now can analyze these issues and provide solutions. This book also helps the reader to acquire a better insight into these complex systems. With the diffusion of computational skills in industry and academia, we see the future of computation in process engineering rather promising. This would allow the better utilization of existing computational knowledge along with limited experimentation efforts.

The content of this book has gelled over the last 10 years through the collaborative research efforts of the authors on the subject. The book is primarily intended to serve students, scientists, and practitioners in process, chemical, mechanical, and metallurgical engineering. However, other engineers, consultants, and scientists concerned with various aspects of multiphase flow systems may also find it useful. Scientists and graduate students who want to learn and excel in DEM and CFD-DEM would find this book helpful. The content of this book can be used in a graduate course on advanced modeling and simulation in chemical engineering or as a complementary book to other engineering areas.

The authors acknowledge the contribution of many colleagues, former, and current students from the University of Tehran. Special thanks is extended to Dr. Zahra Mansourpour, Sedigheh Karimi, Bahram Haddani-Sisakht, and Shahab Golshan who have greatly contributed to some of important results presented in this book. We also express our gratitude to Mohammad Amin Hassani, Yasaman Norouzi, Mohammad Foroughi-Dahr, Mahsa Okhovat, Maryam Karimi, Maryam Sanaie-Moghadam, Hanieh Sotudeh-Gharebagh, Dr. Jaber Shabanian, Dr. Ebrahim Alizadeh, Dr. Rouzbeh Jafari, and Mr. Christian Jordan for their help extended to us during the completion of the book. (INSF) is acknowledged for supporting our research efforts in the multiphase flow laboratory, and process design and simulation research center where experimentations on and simulations of multiphase flow processes are the main concern.

Finally, we should emphasize that much remains to be done in this area and the utilization of CFD-DEM is expected to be increased rather than diminished. Adapting CFD-DEM to new areas will undoubtedly keep scientists and engineers busy for a long time. We can only hope that we have provided a useful base from which to start. The authors hope that this book would serve the industry and academia in the coming years. No human attempt is flawless, including this book. With your help, shortcomings and mistakes can be remedied and corrected. You are kindly requested to send your comments and corrections to [email protected].

Hamid Reza NorouziReza ZarghamiRahmat Sotudeh-GharebaghNavid MostoufiJune 2016, University of Tehran, Tehran, Iran

1Introduction

Industry demand for efficient and faster computational tools has facilitated the development of Computational Fluid Dynamics (CFD). This has allowed the utilization of CFD as a specialized tool to solve mass, momentum, energy, and species conservation equations. Advances in computer technology have now changed the entire frame of CFD modeling, allowing it to be a tool for engineers and scientist to carry out design, simulation, and optimization of various processes. Today, the application of CFD not only covers the conventional engineering fields, such as chemical and mechanical engineering, but is also widely extended to multidisciplinary areas, such as environment and healthcare.

With CFD, fluid and solid particulate phases can be modeled by the “Eulerian–Eulerian” approach, which is a way of looking at the motion of fluid and particles from a continuum point of view. This hypothesis may be true for fluids but it may bring less accurate results when considering solid particles as a continuum. In order to properly model particle motion, the Discrete Element Method (DEM) has been developed, in which the motion of individual particles is tracked in space and time using the Lagrangian approach. This approach is complementary to the Eulerian approach for modeling multiphase flows and is referred to as the “ Eulerian–Lagrangian ” approach, detailed in the following sections.

Multiphase flows exist in many industrial applications such as gas or liquid fluidized bed reactors, fluidized bed dryers, spotted beds, three-phase gas-liquid-solid fluidized beds, pneumatic conveying of solids, and so on. A detailed knowledge of these flows is crucial for design, scale-up, optimization, and troubleshooting of such processes. Although this may be achieved by experimental techniques, modeling can be considered as an alternative tool for exploring different aspects of multiphase flows. Modeling enables us to understand different phenomena occurring in these processes, to perform sensitivity analysis on different input parameters and to test different configurations and operational conditions at lower expense compared with experimental methods. In the following, we discuss the overall view of the modeling of granular and multiphase flows.

1.1 Multiphase Coupling

Phase coupling, in terms of momentum, energy, and mass, is a basic concept in the description of any multiphase flow. The coupling can occur through exchange of momentum, energy, and mass among phases as shown in Figure 1.1. In principle, fluid-particulate properties can be described by position, velocity, size, temperature, and species concentration of fluid and/or particle. While the phenomenological description of multiphase flow can be applied to classify flow characteristics, it also can be used to determine appropriate numerical formulations. In various modes of coupling, depending on the contribution of phases and phenomena, different coupling schemes can be adapted. This may allow independent treatment of phases or simultaneous integration of momentum, heat, and mass exchanges between phases. In general, modeling complexity increases as more effects associated with time and length scales are included in the simulation.

Figure 1.1 Momentum, energy, and mass transport between solid and fluid phases

1.2 Modeling Approaches

Real systems are rather complex in nature and modeling allows analysis and simulation of these systems to be conducted more accurately. Depending on the length scales considered for fluid and particle systems, various combinations of modeling scales can be suggested. These are classified as micro-, meso-, and macro-scale models. In a micro-scale model, trajectories of individual particles are calculated through the equation of particle motion and the fluid length scale is the same as the particle size or even smaller. At the same time, instantaneous flow field around individual particles is calculated. In the meso-scale model, both solid and fluid phases are considered as interpenetrating continua. The conservation equations are solved over a mesh of cells. The size of the cells is small enough to capture main features of the flow, like bubble motions and clusters, and large enough (essentially larger than the size of individual particles) to allow averaging of properties (porosity, interactions, etc.) over the cells. Anderson and Jackson [1] first presented this formulation for fluid-particulate systems. In the macro-scale model, the fluid length scale is in the order of the flow field. This means that motions of the fluid and the assemblage of particles are treated in one dimension based on overall quantities [2]. It is also possible to develop some intermediate models in which the length scales of fluid and solid phases are different. For example, the length scale of solid phase can be kept at the micro-scale while changing the length scale of fluid phase to meso or macro. Under these conditions, the affective interactions in the larger scale can be calculated by averaging the information in the smaller scale.

In multi-scale modeling, the smaller scale model takes into account various interactions (i.e., fluid–particle, particle–particle) in detail. These interaction details can be used with some assumptions and averaging to develop closure laws for calculating the effective interactions (e.g., drag force) in the larger scale model [3]. This allows capture of the essential information needed on the larger scale. Alternatively, calculation of effective interactions can be performed through the local experimental data, if available. Combination of fluid/particle motion with different modeling scales can provide different modeling approaches, as sketched in Figure 1.2 and detailed here:

Micro approach (fluid–micro, particle–micro)

: In this approach, the fluid flow around particles is estimated by the Navier–Stokes equation. Since the forces acting on particles are calculated by integrating stresses on the surface of the particle, the empirical correlation for drag and lift forces are not required. This approach is used in cases where particle inertial force is relatively small (e.g., liquid–particle flow) or the fluid lubricating effect on particles is rather significant (e.g., dense-phase liquid–particle flow). A typical example of such an approach, shown in

Figure 1.2

, is the direct numerical simulation–discrete element method (DNS-DEM).

Meso approach (fluid–meso, particle–meso)

: In this approach, which is shown in

Figure 1.2

and is referred to as the two-fluid model (TFM), in addition to the real fluid, the assemblage of particles is also considered to be the second continuum phase. The flow field is divided into a number of small cells to capture motions of both phases, provided that the cell size is larger than the particle size. The two continuous phases are modeled by applying laws of momentum and mass conservations in each fluid cell, leading to averaged Navier–Stokes and continuity equations. Capability of the TFM in capturing the solid phase motion greatly depends on the closure laws used for this phase. These closure laws always involve some simplifications or are obtained by semi-empirical correlations. While this approach is preferred in commercial packages for its computational simplicity, its effectiveness depends on the constitutive equations and is not easily applicable to all flow conditions. The TFM has been successfully utilized to obtain the flow behavior of various non-reacting and reacting multiphase flows in laboratory, pilot, and industrial scales.

Macro approach (fluid–macro, particle–macro)

: This approach provides a one-dimensional (1D) description of gas-particle flows [4]. The main output of such a model is the pressure drop, which is considered as the sum of pressure drops due to flow of fluid and particles. Usually, a formula for the single phase flow, such as Darcy–Weisbach equation, is used for the fluid pressure drop and that of particles is balanced with the fluid drag formula from the momentum balance. This approach would also allow the calculation of averaged flow properties by empirical correlations that are essential in design and analysis of industrial processes. A typical example of such approach, shown in

Figure 1.2

, is the two-phase model (TPM) in fluidization. In this model, conservation equations are written for bubbles and emulsion, both having the length scale of the system in a fluidized bed.

Macro-micro approach (fluid–macro, particle–micro)

: In this approach, shown in

Figure 1.2

by 1D-DEM, the fluid forces acting on particles are calculated from empirical correlations (e.g., drag and lift) while translational and rotational motions of particles are described based on Newton’s and Euler’s second laws. At very low concentration of particles, effect of particles on the fluid motion can be neglected. However, at higher concentrations, closure laws should be modified to account for the closeness of surrounding particles. Generally, in this approach the flow field, which is considered to change in one dimension, is not divided into cells and additional pressure drop is taken into account to reflect the effect of particles on the fluid motion.

Meso-micro approach (fluid–meso, particle–micro)

: In this approach, referred to as

CFD-DEM

and shown in

Figure 1.2

, the flow field is divided into cells with a size larger than the particle size but still less than the flow field. Effect of motion of particles on the flow of fluid is considered by the volume fraction of each phase and momentum exchange through the drag force. This approach is the focus of this book and is explained in detail in the following sections.

Figure 1.2 Modeling scales in fluid–particulate systems

Let’s consider an example for illustrating the abovementioned approaches for modeling. Various modeling approaches including TPM, TFM, CFD-DEM, and DNS-DEM for a gas-fluidized bed are shown in Figure 1.3. The macro approach TPM, which is a one-dimensional model, is mostly used in industrial applications for long term simulations. For multi-dimensional modeling, the TFM can be used to predict the characteristics of fluidized beds at the meso-scale. The increased accuracy of the model is obtained at the expense of more computational costs and simulations are restricted to shorter periods. Handling the solid particles at a micro scale and fluid at meso scales is carried out in the CFD-DEM approach. While this increases the computational cost, it provides results with a higher resolution when compared with the TFM. If a higher resolution is needed for the fluid phase, DNS-DEM is the choice of modeling considering the fact that it needs higher computational effort. It should be mentioned that applying CFD-DEM, and especially DNS-DEM, is mostly limited to lab scale units.

Figure 1.3 Different modeling approaches for fluid–solid systems

In a gas-solid fluidized bed, various structures (micro, meso, and macro) coexist with different scales, as shown in Figure 1.4. Single particles and individual particles in clusters are typical examples of micro scale phenomena, while small bubbles and clusters are considered meso structures. Large bubbles, as well as the whole reactor, are at macro scale. However, it is important to mention that we are not limited to use the scales of these phenomenological structures in the modeling. Nevertheless, modeling with a finer scale would provide characteristics of that structure and larger ones while coarser scale modeling would provide only an averaged description of finer structures. For example, if using the CFD-DEM model, characteristics of particles, clusters, and bubbles can be captured, while in the TFM only characteristics of clusters and bubbles can be obtained and individual particles cannot be observed.

Figure 1.4 Different scales in a fluid–solid system

1.3 Modeling with DEM

DEM is a type of modeling tool through which the dynamics of a system comprising of a large number of distinct bodies with arbitrary shapes are studied. In granular flows, these distinct bodies are solid particles. Particles may interact with each other through their contact area or interparticle effects. Particles are assumed to be either rigid or deformable, leading to two different formulations of their collision, that is, hard-sphere/event-driven and soft-sphere/time-driven. In the soft-sphere formulation, which is the main focus of this book, particles are allowed to overlap and their contact lasts for a certain period. This allows a particle to be in contact with more than one particle at a time. This formulation is suitable for the motion of particles in both dense and dilute phases at quasi-static and dynamic conditions. Translational and rotational motions of a particle are tracked by integrating Newton’s and Euler’s second laws of motions, respectively. In addition to contact forces between particles, inclusion of other forces, like interparticle and fluid–particle interaction (if the fluid effect is significant), can be performed by introducing proper terms in the equation of motion.

The contact force is calculated according to normal and tangential overlaps of particles (or particle and wall) using a set of force-displacement expressions combined with friction laws. Many force-displacement models, such as linear and non-linear visco-elastic, elasto-plastic, and visco-plastic models have been developed for calculating the contact force between particles depending on the material properties and operating conditions. Particles are surrounded by walls as system boundaries. Among different methods, walls can be introduced to the model by decomposing the actual geometry into triangular or quadrilateral elements. This seems to be the flexible and rather general method to deal with simple to complex geometries in DEM simulations.

All motion and force equations associated with the DEM should be solved using proper integration methods. The explicit integration of these equations, in contrast to the implicit integration, increases the flexibility of the DEM simulation, even though it requires adapting a small time step for integration. A small time step and existence of a large number of interacting particles in the DEM simulation demands a huge computational resource. Without implementing efficient numerical algorithms and parallelization, this modeling approach is restricted to short-time simulations with the number of particles not exceeding 105. Nowadays, the DEM is a powerful technique, allowing scientists and engineers to analyze rather complex systems for which analysis and understanding are not possible by current experimental techniques. In Chapters 2–5 of this book, essential components related to formulation, implementation, and sample application of DEM can be found in detail.

1.4 CFD-DEM Modeling

CFD coupled with DEM is a computational approach used to model fluid–particle systems. In the CFD-DEM, the fluid phase is assumed as a continuum and its meso-scale motion is described by the volume averaged Navier–Stokes equation, while the micro-scale motion of the solid phase is described by Newton’s and Euler’s second laws. Forces acting on a particle are gravity, contact between colliding particles, fluid–particle interaction, and interparticle forces. Normally, there are thousands or millions of distinct bodies in the system for which the equation of motion should be simultaneously solved along with fluid equations over fluid cells. These equations are usually presented for phenomena occurring with different length scales in the system. The flow field of the CFD-DEM is shown in Figure 1.5, which demonstrates the relation between micro- and meso-scales for modeling of a gas-solid system. The coupling between fluid and particulate phases is performed through the local porosity and the mutual fluid–particle interaction forces. Comprehensive reviews of CFD-DEM technique and its applications have been published [5–7].

Figure 1.5