Open AccessComputational Study of Operating Parameters on Performance of Compound Hydrocyclone
Author(s) -
R. Giridhar
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.36878
Subject(s) - hydrocyclone , pressure drop , mechanics , cyclone (programming language) , computational fluid dynamics , cyclonic separation , vortex , turbulence , reynolds stress , streamlines, streaklines, and pathlines , mathematics , materials science , environmental science , meteorology , simulation , physics , engineering , inlet , mechanical engineering , field programmable gate array , embedded system
The dynamics of hydro cyclones is complex, because it is a multiphase flow problem that involves interaction between a discrete phase and multiple continuum phases. The performance of hydro cyclones is evaluated by using Computational Fluid Dynamics (CFD), and it is characterized by the pressure drop, split water ratio, and particle collection efficiency. In this paper, a computational model to improve and evaluate hydro cyclone performance is proposed. Computational turbulence models (renormalization group (RNG) k-ε, Reynolds’s stress model (RSM), and large-eddy simulation (LES)) are implemented, and the accuracy of each for predicting the hydro cyclone behavior is assessed. Four hydro cyclone configurations were analyzed using the RSM model. By analyzing the streamlines resulting from those simulations, it was found that the formation of some vortices and saddle points affect the separation efficiency. Furthermore, the effects of inlet width, cone length, and vortex finder diameter were found to be significant. The cut-size diameter was decreased compared to the Hsieh experimental hydro cyclone. An increase in the pressure drops leads to high values of cut-size and classification sharpness. If the pressure drop increases to twice its original value, the cut-size and the sharpness of classification are reduced to their initial values, respectively.