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Particle-resolved simulations of inertial suspensions of spheres and polyhedrons : analysis and modeling Seyed-Ahmadi, Arman


Particle-laden flows where a dispersion of a solid phase is carried by a fluid phase are at the core of numerous industrial and natural processes, such as fluvial sediment transport and fluidized-bed reactors. The dynamics of each phase is intimately coupled with that of the other phase, leading to the emergence of complex, nontrivial interactions that can span wide ranges of spatial and temporal scales. The focus of this thesis is two-fold; namely, analysis of particle shape effects, and modeling hydrodynamic forces and torques in particle-laden flows. To this end, direct numerical simulations are performed for the generation of high-fidelity data, based on which all analyses of this thesis are carried out. In the first part, we scrutinize the dynamics of an isolated polyhedron, i.e. a cube, in highly inertial regimes and various density ratios. Robust helical motions and wake patterns are found for Reynolds numbers at which a sphere moves rectilinearly. An isolated cube exhibits remarkably larger rotational and lateral motions compared to a sphere, by which the effective drag on the particle is greatly affected. We then extend the analysis to inertial suspensions of cubes, where detailed comparisons are made with their counterpart sphere suspensions for various solid volume fractions. While strong clustering occurs in sphere suspensions, cube suspensions are found to be remarkably more homogeneous, as evident from their microstructure and momentum transfer properties. As demonstrated by their intensive transverse velocity fluctuations, cubes are more likely to break up and escape clusters, thus resisting local accumulation and making suspensions better mixed. In the second part, we develop a novel probability-driven point-particle model for the prediction of hydrodynamic forces and torques based on local microstructure in dense arrays of spheres. Following probabilistic arguments, necessary statistical information is extracted from particle-resolved simulations to construct force/torque-conditioned probability distribution maps, which are in turn used as basis functions for a regressive-type model. We subsequently show that our model is capable of predicting a substantial part of the observed force and torque variations, and is thus conceived to be highly promising for accurate interphase coupling in Euler-Lagrange simulations.

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