In this paper, we review the development of new reduced-order modeling techniques and
discuss their applicability to various problems in computational physics. Emphasis is given …

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 …

Spatiotemporal coherent structures are critical in quantifying the hydrodynamics of dense
gas–solid flows. In this study, two data-driven methods, proper orthogonal decomposition …

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 …

It is well-known that distributed parameter computational fluid dynamics (CFD) models
provide more accurate results than conventional, lumped-parameter unit operation models …

Integration of physics principles with data-driven methods has attracted great attention in
recent few years. In this study, a physics-informed dynamic mode decomposition (piDMD) …

This study applies the spectral proper orthogonal decomposition (SPOD) to analyze the
spatio-temporal characteristics associated with the flow fields of a bubbling fluidized bed …

The numerical treatment of fluid–particle systems is a very challenging problem because of
the complex coupling phenomena occurring between the two phases. Although an accurate …

Data-driven methods are of great interest in studying the hydrodynamics of gas–solid flows.
In this paper, we developed an optimized dynamic mode decomposition with control (DMDc) …

Abstract Classical Computational Fluid Dynamics (CFD) of long-time processes with strongly
separated time scales is computationally extremely demanding if not impossible …