  The numerical treatment of fluid-particle systems is a very challenging
problem because of the complex coupling phenomena occurring between the two
phases. Although accurate mathematical modelling is available to address this
kind of application, the computational cost of the numerical simulations is
very expensive. The use of the most modern high-performance computing
infrastructures could help to mitigate such an issue but not completely fix it.
In this work, we develop a non-intrusive data-driven reduced order model (ROM)
for Computational Fluid Dynamics (CFD) - Discrete Element Method (DEM)
simulations. The ROM is built using the proper orthogonal decomposition (POD)
for the computation of the reduced basis space and the Long Short-Term Memory
(LSTM) network for the computation of the reduced coefficients. We are
interested in dealing both with system identification and prediction. The most
relevant novelties rely on (i) a filtering procedure of the full-order
snapshots to reduce the dimensionality of the reduced problem and (ii) a
preliminary treatment of the particle phase. The accuracy of our ROM approach
is assessed against the classic Goldschmidt fluidized bed benchmark problem.
Finally, we also provide some insights about the efficiency of our ROM
approach.
