Over the past decade, DEM-based simulation has become a promising alternative to
physical measurements of thermal particulate systems. Despite their rapid advancement and …

Independent from their intended purpose, the understanding of structural characteristics of
random packings of particles having defined shapes is important to understand and optimize …

Physics-based simulations are often used to model and understand complex physical
systems in domains like fluid dynamics. Such simulations although used frequently, often …

CFD-DEM simulations have been used extensively to study dense fluid-particle systems. In
the point mass representation of particles in DEM, the modeled drag force plays an …

The knowledge of simple and relatively accurate closure equations for the drag coefficients
of nonspherical particles is very important for Eulerian multiphase numerical codes that …

Developing deterministic neighborhood-informed point-particle closure models using
machine learning has garnered interest recently from the dispersed multiphase flow …

Two deep learning methods, Multi-Layer Perceptron (MLP) network and Convolution Neural
Network (CNN) are evaluated to predict drag forces in dense suspensions of ellipsoidal …

Physics-based simulations are often used to model and understand complex physical
systems in domains such as fluid dynamics. Such simulations, although used frequently …

Three reduced order models are evaluated in their capacity to predict the future state of an
unsteady chaotic flow field. A spatially fully developed flow generated in a random packing …

Additive manufacturing processes have been attracting extensive attention and developing
greatly in recent years. These processes have been widely studied by industrial and …