Journal
POWDER TECHNOLOGY
Volume 378, Issue -, Pages 85-103Publisher
ELSEVIER
DOI: 10.1016/j.powtec.2020.09.071
Keywords
CFD-DEM; Drag model; Fluidization; Statistical moments; TFM
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The study uses the open source code MFIX to conduct CFD-DEM and TFM simulations of a fluidized bed experiment. Results show similar predictive capabilities of both modeling approaches for flow characteristics and particle velocity distribution, but differences in pressure drop and fluctuating quantities predictions. TFM simulations demonstrate more accurate predictions of particle granular temperature, while CFD-DEM simulations perform closer to experimental values. Overall, CFD-DEM simulations take twice as long as TFM simulations.
The open source code MFIX is used to perform CFD-DEM and TFM simulations based on the small scale challenge problem 1 fluidized bed set up. The basic flow features such as the core-annulus structure and slugging dynamics are well predicted by both modeling approaches. Similarly the first four statistical moments of the particle velocity distributions from HsPIV measurements are equally reproduced qualitatively by the models. The predicted mean pressure drop is independent of the drag correlation used in both modeling frameworks but the CFDDEM model gives predictions closer to the experimental values. Predicted fluctuating quantities such as rms and granular temperature show sensitivity to the drag model used. The TFM demonstrates greater accuracy over the CFD-DEM in prediction of the particle granular temperature while the opposite is true for the bubble granular temperature. CFD-DEM simulations on average take twice the time required to perform a TFM simulation. (C) 2020 Elsevier B.V. All rights reserved.
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