277021 Coupled CFD and DEM Simulation of Fluid-Granular Systems

Wednesday, October 31, 2012: 1:50 PM
Conference C (Omni )
Dalibor Jajcevic1, Charles Radeke1, Georg Scharrer1 and Johannes G. Khinast2, (1)Research Center Pharmaceutical Engineering GmbH, Graz, Austria, (2)Institute for Process and Particle Engineering, Graz University of Technology, Graz, Austria

Coupled CFD and DEM Simulation of Fluid-Granular Systems

Dalibor Jajcevic1, Charles Radeke1, Georg Scharrer1, Johannes G. Khinast2

1: Research Center Pharmaceutical Engineering, Graz, Austria

2: Graz University of Technology, Institute for Process and Particles Technology, Austria

Granular flows are extremely important for the pharmaceutical and chemical industry. The understanding of the impact of particle size and related effects on the mean, as well as on the fluctuating flow field, in granular flows is critical for design and optimization of powder processing operations. Computational Fluid Dynamics (CFD) has become essential to the design and development of many engineering devices. Although initially limited to specialized engineering fields, in the last decades it became a commonly-used tool for analyzing many complex technical problems that involves fluid flows, heat transfer, chemical reactions etc. In the last few years Discrete Element Model (DEM) simulations have been increasingly used to study and analyze flows of granular systems with the main emphasis on granular flows where the interaction between particles and a fluid phase may be neglected. Instead only particle-particle interactions are considered. In pharmaceutical industry, fine granular materials (usually smaller than 3 mm) are generally used, but large enough that the gas drag overcomes the gravity. The particles are moved and mixed leading to a non-uniform and unsteady flow situation, which is beneficial for processes such as coating, granulation, draying etc. Thus, a deep understanding of the fluid-particle interaction is important to overcome severe difficulties in the design and scale-up of the industrial devices.

Applying above mentioned simulation techniques a simulation of the fluid-granular systems in a multiphase setup is possible. In the modeling, the drag force in only accelerating force acting on a particle and thus plays an important role in the coupling between gas and solid phases, see Van der Hoef et al. [1] and Wei Du et al. [2]. In literature, several widely used models are available, such as Gidaspow et al. [3], Syamlal and O'Brien [4], Di Felice [5] etc. Authors, such as Wei Du et al. [2], reviewed the models and showed that the Gidaspow model gives the best agreement with the experimental observation both qualitatively and quantitatively. Gidaspow et al. [3] derivate the model combining Ergun [7] equation for dense regimes and a correlation proposed by Wen and Yu [8] for the more dilute regimes. In the last few years common used model is a model proposed by Beetstra et al. [6]. Using a similar approach as Hill et al. [9], Beetstra et al. [6] derived the model based on a wide range of data for Reynolds numbers up to 1000 showing proper limiting behaviour and is therefore believed to be more practical, see Deen et al. [10].


In the first steep of the presented work the model proposed by Gidaspow et al. [3] and the relative new model derivate by Beetstra et al. [6] are investigated. The models are implemented via user function in the commercial CFD code AVL-Fire in an Eulerian two-phase setup (a gas and a solid phase). The user function allows defining the momentum interfacial exchange between the phases, whereas the solid phase is treated as non-moved, therefore a coupling between CFD and DEM code in this steep was not required. Taking into account Ergun [7] equation the pressure drop is calculated and compared with results of the CFD simulation for the Reynolds numbers up to 2000, four particles diameters and three different void fractions. The results show that both models can successfully predict the pressure drop through a porous media composed of monodisperse particles. Nevertheless, the model introduced by Beetstra et al. [6] shows increasing pressure drop behaviour with a decreasing particle diameter, but still in an acceptable range and is further used in this work.


In the second step, a coupled simulation between in-house developed DEM code and the commercial CFD code AVL-Fire was realized. In order to be able to simulate real-life granular systems, a new technology was implemented and a high-performance DEM code was developed based on the newly available “Compute United Device Architecture” (CUDA) technology, see Radeke et al [11]. The DEM code can simulate millions of particles on readily available hardware. The simulations can be directly visualized by a GUI using OpenGL display features. Based on the computational efficiency of our method the development of large-scale simulations is highly convenient. The data exchange between the codes was realized applying AVL Code Coupling Interface (ACCI). ACCI is a software component to perform a co-simulation with an arbitrary number of instances of different simulation programs. The codes concurrently simulate the same time interval and pass information to each other continuously. The results obtained in the simulation were verified using experimental data of Link et al. [12]. The measurement is obtained in an experimental study of the flow in a pseudo-2D spout-fluid bed taking into account different operating conditions. A good agreement with the experimental data confirms the accurateness of used coupling methodology.

1. References

1.       Van der Hoef  M. A., Ye M., van Sint Annaland M., Andrews A. T., Sundaresan S., Kuipers J. A. M., “Multi-Scale Modeling of Gas-Fluidized Beds”, In: Advances in Chemical Engineering, vol. 31, pp. 65

2.       Wei Du, Xiaojun Bao, Jian Xu, Weisheng Wei „Computational fluid Dynamics (CFD) modeling of spouted bed: Assessment of drag coefficient correlations“, Chemical Engineering Science 61, pp. 1401-1420, Elsevier 2005

3.       Gidaspow D, “Multiphase Flow and Fluidization”, Continuum and Kinetic Theory Description, 1994

4.       Syamlal M. and O'Brien, T.J., “Simulation of granular layer inversion in liquid fluidized beds”, International Journal of Multiphase Flow 14, pp. 473-481, 1988

5.       Di Felice, R., “The voidage functions for fluid-particle interaction system”, International Journal of Multiphase Flow 20, pp. 153-159, 1994

6.       Beetstra R., Van der Hoef M. A., Kuipers J. A. M., “Numerical study of segregation using a new drag force correlation for polydisperse systems derived from Lattice-Boltzmann simulations”, Chem. Eng. Sci. 62, pp. 246-255, 2007

7.       Ergun S., “Fluid flow through packed columns”, Chemical Engineering

Progress 48, pp. 89–94, 1952

8.       Wen Y.C. and Yu Y.H., “Mechanics of Fluidization”, Chemical Engineering

Progress Symposium Series 62, pp. 100–111, 1966

9.       Hill R.J., Koch D.L., Ladd J.C., “ Moderate-Reynolds-numbers flows in ordered and random arrays of spheres”, Journal of Fluid Mechanics 448, pp. 243–278, 2001

10.   Deen N.G., Van Sint Annaland M., Van der Hoef  M. A., Kuipers J.A.M., “Reviewof discrete particle modeling of fluidized beds”, Chemical Engineering Science 62, pp. 28-44, 2007

11.   Radeke C, Glasser B., Khinast J., „Large-scale Mixer Simulations Using Massively Parallel GPU Architectures”, Chemical Engineering Science, Volume 65, Issue 24, pp. 6435–6442, 2010

12.   Link J., Zeilstra C., Deen N., Kuipers H., „Validation of a Discrete Particle Model in a 2D Spout-Fluid Bed Using Non-Intrusive Optical Measuring Techniques”, Canadian Journal of Chemical Engineering 82, pp. 30-36, 2004


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