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.

翻译：流体-固体系统在许多工业过程中具有重要研究价值。从计算角度来看，此类系统的模拟成本极高，尤其在需要研究大量参数配置时。针对这一背景，我们构建了一种基于本征正交分解与插值（PODI）方法的非侵入式数据驱动降阶模型（ROM），用于计算流体动力学（CFD）-离散元方法（DEM）模拟。该方法的主要创新点在于：(i) ROM与有限体积（FV）方法的结合；(ii) 关于本征正交分解（POD）模态数量及训练集基数对ROM精度影响的数值敏感性分析；(iii) 针对斯托克斯数的参数化研究。我们在流化床基准问题上对ROM进行测试，通过与全阶模型（FOM）在欧拉量（流体体积分数）和拉格朗日量（颗粒位置与速度）上的结果对比评估模型精度，并讨论了ROM方法的计算效率。