A Non-Intrusive Data-Driven Reduced Order Model for Parametrized CFD-DEM Numerical Simulations

TL;DR

This paper introduces a non-intrusive, data-driven reduced order model combining POD and interpolation for efficient CFD-DEM simulations, validated on fluidized bed problems with promising accuracy and efficiency.

Contribution

It presents a novel ROM approach integrating PODI with CFD-DEM, including sensitivity analysis and parametric study, enhancing simulation efficiency for fluid-solid systems.

Findings

ROM achieves high accuracy compared to full-order models

Significant reduction in computational cost

Effective parametric analysis with respect to Stokes number

Abstract

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.