Analysis and Development of Novel Data-driven Drag Models based on
Direct Numerical Simulations of Fluidized Beds
- kun luo,
- Dong Wang,
- Tai Jin,
- Shuai Wang,
- Zhuo Wang,
- Junhua Tan,
- Jianren Fan
Abstract
Drag force is essential to dense flows, but 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 drag model based on the
artificial neural network is developed. Given the complicated structure
of this model, a simplified drag model is also formulated by directly
fitting the samples. The drag force predicted by both models agrees
excellently with the DNS data and is much more accurate than that
predicted by existing models.