Abstract
In the present study, multi-objective optimization of a cyclone vortex finder is performed in three steps. In the first step, collection efficiency (η) and the pressure drop (Δ p) in a set of cyclones with different vortex finder shapes are numerically investigated using CFD techniques. Two meta-models based on the evolved group method of data handling (GMDH) type neural networks are obtained in the second step, for modelling of η and Δ p with respect to geometrical design variables. Finally, using the obtained polynomial neural networks, multi-objective genetic algorithms are used for Pareto-based optimization of a vortex finder considering two conflicting objectives, η and Δ p.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
