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 …

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 …

Understanding the dynamics of granular flows is of great significance in various engineering
fields but challenging because of flow complexity. The discrete element method (DEM) is …

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) …

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

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 …

Data assimilation is crucial in a wide range of applications, but it often faces challenges such
as high computational costs due to data dimensionality and incomplete understanding of …

Numerous products are manufactured through powder mixing. Understanding the mixing
mechanism is essential to improve product quality. Convection, diffusion, and shear are well …

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) …