  Particle-in-Cell (PIC) approach for modeling dense granular flows has gained
popularity in recent years due to its time to solution efficiency. The
methodology is useful for modeling large-scale systems with a relatively lower
computational cost. However, the method requires the definition of several
empirical parameters whose effects are not well understood. A systematic
approach to identify sensitivities and optimal settings of these parameters is
required. Already, it is known that the choice of these parameters depends on a
problem's flow regime. For instance, parameter values would be chosen
differently for a settling bed or a fluidized bed. In this study, five
different PIC model parameters were selected for calibration when applied to
the case of particles settling in a dense medium. PIC implementation from the
open-source software MFiX (MFiX-PIC) was used. This study extends the earlier
work to assess the five model parameter settings using deterministic
calibration by employing a statistical calibration methodology commonly
referred as Bayesian calibration. Results from deterministic calibration are
compared with Bayesian calibration, and up to 6.5 fold improvement in
prediction accuracy is observed with the latter approach.
