This review is motivated by the fast progress in our understanding of the physics of particle-
laden turbulence in the last decade, partly due to the tremendous advances of measurement …

Using what has become a celebrated catchphrase, Philip W. Anderson once wrote that
“more is different”(Science, Vol. 177, Issue 4047, pp. 393–396, 1972). First formulated in the …

Machine learning (ie, modern data-driven optimization and applied regression) is a rapidly
growing field of research that is having a profound impact across many fields of science and …

We present a physics-inspired neural network (PINN) model for direct prediction of
hydrodynamic forces and torques experienced by individual particles in stationary arrays of …

Increasing the global energy demand motivates the search for renewable and clean energy
resources. Fischer-Tropsch synthesis (FTS) is one of these sources, which converts syngas …

This study investigates the thermal conductivity of a constructal theory-based heat pipe and
presents the predction of a lithium-ion battery's thermal behaviour during charge and …

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 …

In computational fluid dynamics (CFD) simulations of thermochemical biomass conversion
using the discrete element method (DEM), the need to establish adequate coupling …

This article deals with approximating steady-state particle-resolved fluid flow around a fixed
particle of interest under the influence of randomly distributed stationary particles in a …

The undisturbed flow of a particle is of fundamental importance since it controls both the
undisturbed flow force and the perturbation force (which includes quasisteady, added-mass …