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(62e) DEM Simulations of Industrial Sized Fluidized Bed with Particle Size Distribution Using Coarse Grain Model

Baran, O., Siemens PLM
Newale, A., CD-adapco

Fluidized beds are common in many industries, notably chemical processing and energy production. Not only the design of these beds, but also their operation can be a challenging task due to the complexity of processes and limited ability for direct measurements within the bed. Numerical simulation of fluidization can reveal many behavioral aspects of a fluidized beds. An Eulerian or a Lagrangian approach (Discrete Element Modeling) are used in simulation.

Particle size distribution is known to be important to the behavior of fluidized bed reactors. For example, moderate increase in amount of smaller size particles, fines, can lead to better performance, while excessive content of fine particles can amplify the role of inter-particle forces and decrease reactor performance. This paper introduces the numerical method for simulating industrial scale reactors involving particles with wide size distribution and reports the results for large-scale parallel simulations of fluidized bed using Discrete Element Method (DEM) fully coupled with fluid flow model using STAR-CCM+ software developed by CD-adapco.

Our approach build on earlier work introducing the DEM coarse grain model [1]. Our implementation introduces feature to facilitate simulation of poly-disperse particles and allowing distribution of particle size scaling factors. Results for simulating more than 1e9 particles with wide and bimodal size distribution are analyzed and compared with theoretical predictions and reported in the literature trends. Authors believe that this generalized coarse grain model will be able to achieves a balance between accuracy (a feature of DEM methods) and speed (a feature of computation particle approaches) to enable practical design and scale-up workflow.

[1] Large-scale discrete element modeling in a fluidized bed. Sakai, Mikio, et al. 2010, International Journal for Numerical Methods in Fluids, Vol. 64, pp. 1319-1335.


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