LAUSR

Fluidization of non-spherical particles is very common in petroleum engineering. Understanding the complex phenomenon of non-spherical particle flow is of great significance. In this paper, coupled with two-fluid model, the drag coefficient correlation based on artificial neural network was applied in the simulations of a bubbling fluidized bed filled with non-spherical particles. The simulation results were compared with the experimental data from the literature. Good agreement between the experimental data and the simulation results reveals that the modified drag model can accurately capture the interaction between the gas phase and solid phase. Then, several cases of different particles, including tetrahedron, cube, and sphere, together with the nylon beads used in the model validation, were employed in the simulations to study the effect of particle shape on the flow behaviors in the bubbling fluidized bed. Particle shape affects the hydrodynamics of non-spherical particles mainly on microscale. This work can be a basis and reference for the utilization of artificial neural network in the investigation of drag coefficient correlation in the dense gas–solid two-phase flow. Moreover, the proposed drag coefficient correlation provides one more option when investigating the hydrodynamics of non-spherical particles in the gas–solid fluidized bed.