Development of a reduced-order model for large-scale Eulerian-Lagrangian simulations

Multiphase flows with solid particles are commonly encountered in various industries. The CFD-DEM method is extensively used to simulate their dynamical behavior. However, the application of the CFD-DEM method to simulate industrial-scale powder processes unavoidably leads to huge computa-tional costs. With the aim of overcoming this issue, we propose a nonintrusive reduced-order model for Eulerian-Lagrangian simulations (ROM-EL) to efficiently reproduce gas-solid flow in fluidized beds. In the model, a Lanczos based proper orthogonal decomposition (LPOD) is newly employed to efficiently generate a set of POD bases. After the numerical snapshots are projected onto the reduced space spanned by the POD bases, a series of multidimensional functions of POD coefficients are constructed using a sur-rogate interpolation method. To demonstrate the effectiveness of this model, validation studies are per-formed based on the simulations of a fluidized bed. The macroscopic properties, such as the particle distribution, bed height, pressure drop, and distribution of bubble size, are shown to agree well in the CFD-DEM model and ROM-EL. Further, our proposed ROM-EL reduces the computational cost by several orders of magnitude compared with the CFD-DEM simulation. Accordingly, the ROM-EL could signifi-cantly contribute to the progress of modeling and simulation for industrial granular flows. (c) 2022 The Society of Powder Technology Japan. Published by Elsevier BV and The Society of Powder Technology Japan. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).

Automated summary

This study proposes a nonintrusive reduced-order model (ROM-EL) for efficiently simulating gas-solid fluidized beds, and its effectiveness is demonstrated through validation studies. The proposed model significantly reduces the computational cost compared to the CFD-DEM method.