文献类型：期刊文献

英文题名：Simulation and experimental study of effect of vortex finder structural parameters on cyclone separator performance

作者：Zhang, Zheng-Wei[1];Li, Qing[1];Zhang, Yan-Hong[1];Wang, Hua-Lin[1]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China

年份：2022

卷号：286

外文期刊名：SEPARATION AND PURIFICATION TECHNOLOGY

收录：;EI(收录号：20220211455645);WOS:【SCI-EXPANDED(收录号:WOS:000782997300004)】；

语种：英文

外文关键词：Tangential velocity index; Cyclone separation; Vortex finder; Artificial neural network

摘要：This study attempts to clarify the correlation between the structural parameters of vortex finders (diameter ratio and insertion depth) and the strong turbulent flow field and performance of cyclone separators. To elucidate the effects due to the overflow pipe diameter, insertion depth, and Reynolds number on the tangential velocity index (n) and separation efficiency, 0.3D, 0.4D, 0.5D, 0.6D, and 0.7D (where D is the diameter of the cyclone separator's cylindrical section) were selected as the vortex finder diameter and insertion depth, which were studied using three methods: computational fluid dynamics (CFD) software Fluent, experiments, and artificial neural network simulations. With Fluent, the Reynolds stress model (RSM) of turbulence was adopted to describe transport equations, and the discrete phase model (DPM) for two-phase flow was used to calculate the efficiency and separate particles at different inlet velocities. Experiments were conducted to measure the operational pressure drop and separation efficiency of the system; subsequently, the results were used to validate the CFD calculation results. Finally, a 5-5-1 three-layer artificial neural network was established based on 336 sets of data for values of n obtained from numerical simulations. In addition, a backpropagation (BP) artificial neural network model was employed to predict the correlation between the structural and operational parameters of the cyclone separator and n values. The obtained results indicate that the BP artificial neural network can accurately predict the distribution of n values within the flow field of the cyclone. Increasing the diameter of the vortex finder with the same inlet velocity can reduce the total pressure drop due to the cyclone, thereby reducing the kinetic energy loss and improving the separation efficiency of fine particles. Increasing the insertion depth of the vortex finder leads to an increase in the values of n. Considering the stability of the flow field structure and energy dissipation, an S/D value between 0.5 and 0.6 is preferred.