Analysis and development of novel data-driven drag models based on direct numerical simulations of fluidized beds

Abstract

Drag force is essential to dense flows, but the accurate and robust drag model is still an open issue. Direct numerical simulations of a shallow and a deep bubbling bed of moving rigid spheres have been carried out in the present work by using an immersed boundary method, and big data are produced. It turns out that the drag force in fluidized beds is typically underestimated by traditional drag models which depend only on the particle Reynolds number and the void fraction. With two additional parameters representing the velocity fluctuation and position fluctuation of particles introduced, a novel drag model based on the artificial neural network is developed, and a simplified version is also formulated. The drag force predicted by both models agrees excellently with the DNS data and is much more accurate than that predicted by existing models.