Based on the principle of active anti-wear method, seven metal anti-wear devices of types A through G were designed and numerically evaluated by comparing the erosion distribution of the local water wall surface in this work. The result shows: an approximate vertical triangle structure with an inclined upper surface and a vertical lower surface, is the most ideal structure for reducing the erosion rate. The type G and ash deposition can be combined into this ideal structure. The simulation results based on the type G show that the erosion rate increases correspondingly with the increasing inlet velocity and particle size and is somewhat mitigated by the addition of cohesive particles. The height of the ash deposition zone decreases with decreasing particle diameter and proportion of cohesive particles. The type G is preferred to be tested on the CFB boiler for half a year, and achieved a good anti-wear effect.

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.