PR-DNS investigation on momentum and heat transfer of two interactive non-spherical particles in a fluid

Abstract

Particle-resolved direct numerical simulations (PR-DNS) are carried out to investigate the momentum (quantified by the drag coefficient, Cd) and heat (quantified by the average Nusselt number, Nu) transfer of two interactive non-spherical porous particles in a fluid. The leading particle (relevant parameters marked by a subscript ‘L') is a spheroid with different shapes and porosities, and the trailing particle (relevant parameters marked by a subscript ‘T') is a sphere. The numerical model is firstly well validated against previously published data and then the effects of the leading particle aspect ratio (ArL), orientation (θL), porosity (εL), distance (L) and Reynolds number (Re) are stressed, respectively, on the CdT and NuT of the trailing one. New findings from the current numerical results are: CdT increases when increasing θL for a leading oblate spheroid but decreases with θL for a leading prolate spheroid. When θL = 0°, CdT increases with increasing ArL but the opposite trend is found when θL = 90°. When θL = 45° and the distance between the two particles is small, CdT increases with the increase of ArL. However, when the distance between the two interactive particles gets larger, CdT first decreases and then increases with ArL. When the leading particle is a spheroid and the two interactive particles are far away from each other, NuT increases first and then decreases with increasing θL. When the leading particle is a spheroid and the two interactive particles are close to each other, the changing trend of NuT with θL can be more greatly influenced by εL. That is, when εL = 0.9, NuT increases with θL for a leading oblate spheroid but decreases with θL for a leading prolate spheroid. On the contrary, when εL = 0 and εL = 0.5, NuT increases first and then decreases with θL for both leading oblate and prolate spheroids. When θL = 45°and the two interactive particles are close to each other, a large εL of the leading spheroid plays an important role in affecting NuT which makes it drop significantly. When θL = 45°and the two interactive particles are far away from each other, and the effects of a leading inclined spheroid on both CdT and NuT are weaker than that of a leading sphere. Generally speaking, both CdT and NuT decrease with increasing εL. At last, a back propagation neural network (BPNN) model is established in this study for prediction purposes.