(140d) System-Size Independent Validation of CFD-DEM for Non-Cohesive Particles | AIChE

(140d) System-Size Independent Validation of CFD-DEM for Non-Cohesive Particles

Authors 

LaMarche, C. Q. - Presenter, Particulate Solid Research, Inc.
Liu, P. - Presenter, University of Colorado at Boulder
Kellogg, K. - Presenter, University of Colorado at Boulder
Weimer, A. W. - Presenter, University of Colorado at Boulder
Hrenya, C. M. - Presenter, University of Colorado at Boulder

Fluid-particle systems can be modeled using Computational Fluid Dynamics coupled with Discrete Element Modeling (CFD-DEM), in which the gas phase is treated as a continuum and the particle phase as discrete entities. CFD-DEM is useful in elucidating much of the physics underlying fluid-particle systems. However, simulating every particle, as is done in CFD-DEM, leads to computational bottlenecks, limiting simulations to a small number of particles (generally <106) compared to realistic systems (>1013). Previous CFD-DEM validation has relied on system-size dependent parameters (e.g. bubble properties, particle velocities and local bed voidage), which require the number of particles in the experiments and simulations is matched. Correspondingly, quantitative validation to date has been limited to comparison with experiments of large (generally Geldart’s group D, ≳ 1mm diameter) particles or small and thin rectangular, beds. The restrictions to thin beds can result in biased validation when wall effects are significant, if not dominating. Here, we identify a system-size independent measurement, the defluidization curve, which can be used to quantitatively compare large-scale experiments with CFD-DEM predictions from a much smaller scale. This size-independent measurement is demonstrated via a direct comparison between small-scale DEM simulations and lab-scale data for gas-fluidized Group B particles.  This method of validation is valuable because it allows for validation of DEM models (e.g. particle shape, van der Waals, and etc.) without resorting to unrealistically large computational times or impractically small system sizes.