  The investigation of fluid-solid systems is very important in a lot of
industrial processes. From a computational point of view, the simulation of
such systems is very expensive, especially when a huge number of parametric
configurations needs to be studied. In this context, we develop a non-intrusive
data-driven reduced order model (ROM) built using the proper orthogonal
decomposition with interpolation (PODI) method for Computational Fluid Dynamics
(CFD) -- Discrete Element Method (DEM) simulations. The main novelties of the
proposed approach rely in (i) the combination of ROM and FV methods, (ii) a
numerical sensitivity analysis of the ROM accuracy with respect to the number
of POD modes and to the cardinality of the training set and (iii) a parametric
study with respect to the Stokes number. We test our ROM on the fluidized bed
benchmark problem. The accuracy of the ROM is assessed against results obtained
with the FOM both for Eulerian (the fluid volume fraction) and Lagrangian
(position and velocity of the particles) quantities. We also discuss the
efficiency of our ROM approach.
