Geometry optimization of a deswirler for cyclone separator in terms of pressure drop using CFD and artificial neural network

Abstract

Four main geometrical parameters of a deswirler (core diameter, number of vanes, height of vanes and leading edge angle) for cyclone separators have been optimized using CFD and artificial neural network. The results indicated that the most significant geometrical parameters of the deswirler are the number of vanes, the vane angle and the vane height. A new optimized deswirler geometry was obtained using the genetic algorithms and its effects on the flow field, pressure losses and cyclone collection efficiency were numerically investigated. The deswirler positively affects the flow field within a cyclone. It dramatically reduces tangential velocities in the vortex finder and only slightly (by 4.5%) decreases maximum tangential velocities in the separation zone. The deswirler also reduces the length of the inner vortex, redistributes uniformly axial velocities at the vortex finder outlet and prevents backward flow. Additionally, the deswirler converts the dynamic energy of the swirling flow into pressure and allows pressure recovery. It reduces pressure losses in the vortex finder by 95.67% that leads to 43.17% reduction in total pressure drop and slightly decreases the separation efficiency for some particle diameters, increasing the cyclone cut size from 1.5 to 1.72 μm.